import numpy as np
import scipy.stats as stats
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import urllib
import requests
from io import StringIO
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
plt.style.use('fivethirtyeight')
We use different datasets from the Worl Bank and the Migration Policy Institute. We use variables that measure economic performance, inequality, life expectancy, popultion, remittances and aid. We analyze only the three countries of the northern triangle; El Salvador, Guatemala and Honduras. These countries have the higher levels of emigration to the U.S. after Mexico in Latin America.
World bank data: governance, trade, labor conditions, economic performance, education and life expectancy
Migration Policy Institute: Migration the countries of the northern triangle of Central America
Time Series from 1980-2015
OLS, Lasso, Elastic Net, Regression Tree, Random Forrest, PCA and KNN
Our dependent variable has missing values, this may affect our results in the different estimations we are running. However, migration data from these countries is scarce and we are interested to analyze this topic even with the issues described. The results are enlightening, anyway.
It is fair to say, this is merely an exploratory attempt to compare Linear and Machine Learning models on the predictors of migration in the Northern Triangle of Central America. Further analysis should make efforts to use better secondary data and fix the collinearity issues described below.
The best predictors according to the different models conducted are related to indicators of economic performance, population growth, remittances, trade, death rates and inequality.
Here we read, revise and clean each of the datasets separately and then we merge them to choose our main variables. We also drop variables with too many missing observations. For the rest of the variables we replace the missing values with the mean. Finally, we convert the variables to more appropriate formats.
mig1 = pd.read_csv('/User directory+/MPI-Data-Hub-Region-birth_1960-2015_1.csv')
mig2 = pd.read_csv('/User directory+/Data_Extract_From_Education_Statistics_-_All_Indicators.csv')
mig3 = pd.read_csv('/User directory+/Data_Extract_From_Poverty_and_Equity_Database-3.csv')
mig4 = pd.read_csv('/User directory+/Data_Extract_From_Health_Nutrition_and_Population_Statistics_Population_estimates_and_projections.csv')
mig5 = pd.read_csv('/User directory+/Data_Extract_From_World_Development_Indicators-9.csv')
mig1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 3 columns): Year 108 non-null int64 Country of Birth 108 non-null object Migration 39 non-null float64 dtypes: float64(1), int64(1), object(1) memory usage: 2.6+ KB
mig1
| Year | Country of Birth | Migration | |
|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 |
| 1 | 1980 | Guatemala | 0.886027 |
| 2 | 1980 | Honduras | 1.076884 |
| 3 | 1981 | El Salvador | NaN |
| 4 | 1981 | Guatemala | NaN |
| 5 | 1981 | Honduras | NaN |
| 6 | 1982 | El Salvador | NaN |
| 7 | 1982 | Guatemala | NaN |
| 8 | 1982 | Honduras | NaN |
| 9 | 1983 | El Salvador | NaN |
| 10 | 1983 | Guatemala | NaN |
| 11 | 1983 | Honduras | NaN |
| 12 | 1984 | El Salvador | NaN |
| 13 | 1984 | Guatemala | NaN |
| 14 | 1984 | Honduras | NaN |
| 15 | 1985 | El Salvador | NaN |
| 16 | 1985 | Guatemala | NaN |
| 17 | 1985 | Honduras | NaN |
| 18 | 1986 | El Salvador | NaN |
| 19 | 1986 | Guatemala | NaN |
| 20 | 1986 | Honduras | NaN |
| 21 | 1987 | El Salvador | NaN |
| 22 | 1987 | Guatemala | NaN |
| 23 | 1987 | Honduras | NaN |
| 24 | 1988 | El Salvador | NaN |
| 25 | 1988 | Guatemala | NaN |
| 26 | 1988 | Honduras | NaN |
| 27 | 1989 | El Salvador | NaN |
| 28 | 1989 | Guatemala | NaN |
| 29 | 1989 | Honduras | NaN |
| ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 |
| 79 | 2006 | Guatemala | 5.343950 |
| 80 | 2006 | Honduras | 5.783592 |
| 81 | 2007 | El Salvador | 18.448273 |
| 82 | 2007 | Guatemala | 5.077445 |
| 83 | 2007 | Honduras | 6.034761 |
| 84 | 2008 | El Salvador | 18.237120 |
| 85 | 2008 | Guatemala | 5.240451 |
| 86 | 2008 | Honduras | 6.339264 |
| 87 | 2009 | El Salvador | 19.096906 |
| 88 | 2009 | Guatemala | 5.539466 |
| 89 | 2009 | Honduras | 6.338030 |
| 90 | 2010 | El Salvador | 20.105788 |
| 91 | 2010 | Guatemala | 5.639487 |
| 92 | 2010 | Honduras | 6.964149 |
| 93 | 2011 | El Salvador | 20.886863 |
| 94 | 2011 | Guatemala | 5.653971 |
| 95 | 2011 | Honduras | 6.437598 |
| 96 | 2012 | El Salvador | 20.945491 |
| 97 | 2012 | Guatemala | 5.586203 |
| 98 | 2012 | Honduras | 6.743448 |
| 99 | 2013 | El Salvador | 20.560594 |
| 100 | 2013 | Guatemala | 5.750461 |
| 101 | 2013 | Honduras | 6.798242 |
| 102 | 2014 | El Salvador | 21.537939 |
| 103 | 2014 | Guatemala | 5.716933 |
| 104 | 2014 | Honduras | 7.389157 |
| 105 | 2015 | El Salvador | 22.073593 |
| 106 | 2015 | Guatemala | 5.675817 |
| 107 | 2015 | Honduras | 7.418273 |
108 rows × 3 columns
print mig1.isnull().sum()
Year 0 Country of Birth 0 Migration 69 dtype: int64
mig1.ix[mig1.Year==1981, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1982, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1983, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1984, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1985, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1986, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1987, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1988, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1989, 'Migration'] = 1.34203879
mig1.ix[mig1.Year==1991, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1992, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1993, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1994, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1995, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1996, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1997, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1998, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==1999, 'Migration'] = 4.516020138
mig1.ix[mig1.Year==2001, 'Migration'] = 7.56872769
mig1.ix[mig1.Year==2002, 'Migration'] = 7.56872769
mig1.ix[mig1.Year==2003, 'Migration'] = 7.56872769
mig1.ix[mig1.Year==2004, 'Migration'] = 7.56872769
mig1.ix[mig1.Year==2005, 'Migration'] = 7.56872769
mig1
| Year | Country of Birth | Migration | |
|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 |
| 1 | 1980 | Guatemala | 0.886027 |
| 2 | 1980 | Honduras | 1.076884 |
| 3 | 1981 | El Salvador | 1.342039 |
| 4 | 1981 | Guatemala | 1.342039 |
| 5 | 1981 | Honduras | 1.342039 |
| 6 | 1982 | El Salvador | 1.342039 |
| 7 | 1982 | Guatemala | 1.342039 |
| 8 | 1982 | Honduras | 1.342039 |
| 9 | 1983 | El Salvador | 1.342039 |
| 10 | 1983 | Guatemala | 1.342039 |
| 11 | 1983 | Honduras | 1.342039 |
| 12 | 1984 | El Salvador | 1.342039 |
| 13 | 1984 | Guatemala | 1.342039 |
| 14 | 1984 | Honduras | 1.342039 |
| 15 | 1985 | El Salvador | 1.342039 |
| 16 | 1985 | Guatemala | 1.342039 |
| 17 | 1985 | Honduras | 1.342039 |
| 18 | 1986 | El Salvador | 1.342039 |
| 19 | 1986 | Guatemala | 1.342039 |
| 20 | 1986 | Honduras | 1.342039 |
| 21 | 1987 | El Salvador | 1.342039 |
| 22 | 1987 | Guatemala | 1.342039 |
| 23 | 1987 | Honduras | 1.342039 |
| 24 | 1988 | El Salvador | 1.342039 |
| 25 | 1988 | Guatemala | 1.342039 |
| 26 | 1988 | Honduras | 1.342039 |
| 27 | 1989 | El Salvador | 1.342039 |
| 28 | 1989 | Guatemala | 1.342039 |
| 29 | 1989 | Honduras | 1.342039 |
| ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 |
| 79 | 2006 | Guatemala | 5.343950 |
| 80 | 2006 | Honduras | 5.783592 |
| 81 | 2007 | El Salvador | 18.448273 |
| 82 | 2007 | Guatemala | 5.077445 |
| 83 | 2007 | Honduras | 6.034761 |
| 84 | 2008 | El Salvador | 18.237120 |
| 85 | 2008 | Guatemala | 5.240451 |
| 86 | 2008 | Honduras | 6.339264 |
| 87 | 2009 | El Salvador | 19.096906 |
| 88 | 2009 | Guatemala | 5.539466 |
| 89 | 2009 | Honduras | 6.338030 |
| 90 | 2010 | El Salvador | 20.105788 |
| 91 | 2010 | Guatemala | 5.639487 |
| 92 | 2010 | Honduras | 6.964149 |
| 93 | 2011 | El Salvador | 20.886863 |
| 94 | 2011 | Guatemala | 5.653971 |
| 95 | 2011 | Honduras | 6.437598 |
| 96 | 2012 | El Salvador | 20.945491 |
| 97 | 2012 | Guatemala | 5.586203 |
| 98 | 2012 | Honduras | 6.743448 |
| 99 | 2013 | El Salvador | 20.560594 |
| 100 | 2013 | Guatemala | 5.750461 |
| 101 | 2013 | Honduras | 6.798242 |
| 102 | 2014 | El Salvador | 21.537939 |
| 103 | 2014 | Guatemala | 5.716933 |
| 104 | 2014 | Honduras | 7.389157 |
| 105 | 2015 | El Salvador | 22.073593 |
| 106 | 2015 | Guatemala | 5.675817 |
| 107 | 2015 | Honduras | 7.418273 |
108 rows × 3 columns
mig1.rename(columns={
'Country of Birth': 'Country'
}, inplace=True)
mig1
| Year | Country | Migration | |
|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 |
| 1 | 1980 | Guatemala | 0.886027 |
| 2 | 1980 | Honduras | 1.076884 |
| 3 | 1981 | El Salvador | 1.342039 |
| 4 | 1981 | Guatemala | 1.342039 |
| 5 | 1981 | Honduras | 1.342039 |
| 6 | 1982 | El Salvador | 1.342039 |
| 7 | 1982 | Guatemala | 1.342039 |
| 8 | 1982 | Honduras | 1.342039 |
| 9 | 1983 | El Salvador | 1.342039 |
| 10 | 1983 | Guatemala | 1.342039 |
| 11 | 1983 | Honduras | 1.342039 |
| 12 | 1984 | El Salvador | 1.342039 |
| 13 | 1984 | Guatemala | 1.342039 |
| 14 | 1984 | Honduras | 1.342039 |
| 15 | 1985 | El Salvador | 1.342039 |
| 16 | 1985 | Guatemala | 1.342039 |
| 17 | 1985 | Honduras | 1.342039 |
| 18 | 1986 | El Salvador | 1.342039 |
| 19 | 1986 | Guatemala | 1.342039 |
| 20 | 1986 | Honduras | 1.342039 |
| 21 | 1987 | El Salvador | 1.342039 |
| 22 | 1987 | Guatemala | 1.342039 |
| 23 | 1987 | Honduras | 1.342039 |
| 24 | 1988 | El Salvador | 1.342039 |
| 25 | 1988 | Guatemala | 1.342039 |
| 26 | 1988 | Honduras | 1.342039 |
| 27 | 1989 | El Salvador | 1.342039 |
| 28 | 1989 | Guatemala | 1.342039 |
| 29 | 1989 | Honduras | 1.342039 |
| ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 |
| 79 | 2006 | Guatemala | 5.343950 |
| 80 | 2006 | Honduras | 5.783592 |
| 81 | 2007 | El Salvador | 18.448273 |
| 82 | 2007 | Guatemala | 5.077445 |
| 83 | 2007 | Honduras | 6.034761 |
| 84 | 2008 | El Salvador | 18.237120 |
| 85 | 2008 | Guatemala | 5.240451 |
| 86 | 2008 | Honduras | 6.339264 |
| 87 | 2009 | El Salvador | 19.096906 |
| 88 | 2009 | Guatemala | 5.539466 |
| 89 | 2009 | Honduras | 6.338030 |
| 90 | 2010 | El Salvador | 20.105788 |
| 91 | 2010 | Guatemala | 5.639487 |
| 92 | 2010 | Honduras | 6.964149 |
| 93 | 2011 | El Salvador | 20.886863 |
| 94 | 2011 | Guatemala | 5.653971 |
| 95 | 2011 | Honduras | 6.437598 |
| 96 | 2012 | El Salvador | 20.945491 |
| 97 | 2012 | Guatemala | 5.586203 |
| 98 | 2012 | Honduras | 6.743448 |
| 99 | 2013 | El Salvador | 20.560594 |
| 100 | 2013 | Guatemala | 5.750461 |
| 101 | 2013 | Honduras | 6.798242 |
| 102 | 2014 | El Salvador | 21.537939 |
| 103 | 2014 | Guatemala | 5.716933 |
| 104 | 2014 | Honduras | 7.389157 |
| 105 | 2015 | El Salvador | 22.073593 |
| 106 | 2015 | Guatemala | 5.675817 |
| 107 | 2015 | Honduras | 7.418273 |
108 rows × 3 columns
mig1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 3 columns): Year 108 non-null int64 Country 108 non-null object Migration 108 non-null float64 dtypes: float64(1), int64(1), object(1) memory usage: 2.6+ KB
mig2
| Time | Time Code | Country | Country Code | Enrolment in tertiary education per 100,000 inhabitants, both sexes [UIS.TE_100000.56] | GDP per capita (constant 2005 US$) [NY.GDP.PCAP.KD] | Population, ages 0-14 (% of total) [SP.POP.0014.TO.ZS] | Population, ages 15-64 (% of total) [SP.POP.1564.TO.ZS] | Primary completion rate, both sexes (%) [SE.PRM.CMPT.ZS] | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | YR1980 | El Salvador | SLV | .. | 2572.813235 | 43.742478 | 52.756733 | .. |
| 1 | 1980 | YR1980 | Guatemala | GTM | .. | 2560.782037 | 45.444923 | 51.602977 | 33.9041481 |
| 2 | 1980 | YR1980 | Honduras | HND | 713.5259399 | 1655.946421 | 46.957200 | 49.818337 | 44.57500076 |
| 3 | 1981 | YR1981 | El Salvador | SLV | .. | 2267.095959 | 43.481122 | 52.948845 | 46.45079041 |
| 4 | 1981 | YR1981 | Guatemala | GTM | 493.2778625 | 2509.736778 | 45.617358 | 51.409643 | 33.95742035 |
| 5 | 1981 | YR1981 | Honduras | HND | 821.0927124 | 1645.846419 | 46.892259 | 49.886066 | .. |
| 6 | 1982 | YR1982 | El Salvador | SLV | 999.5952759 | 2092.554425 | 43.204606 | 53.154795 | 49.3827095 |
| 7 | 1982 | YR1982 | Guatemala | GTM | .. | 2357.368296 | 45.771834 | 51.239138 | 33.8807106 |
| 8 | 1982 | YR1982 | Honduras | HND | 864.4645996 | 1573.671559 | 46.745647 | 50.043171 | 49.94493866 |
| 9 | 1983 | YR1983 | El Salvador | SLV | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.6135788 |
| 10 | 1983 | YR1983 | Guatemala | GTM | 571.4475708 | 2236.567544 | 45.891347 | 51.104597 | 35.13737869 |
| 11 | 1983 | YR1983 | Honduras | HND | 868.4456787 | 1512.185833 | 46.554395 | 50.246028 | .. |
| 12 | 1984 | YR1984 | El Salvador | SLV | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.6973114 |
| 13 | 1984 | YR1984 | Guatemala | GTM | 578.3195801 | 2189.829730 | 45.951383 | 51.025182 | 36.57794952 |
| 14 | 1984 | YR1984 | Honduras | HND | 874.7142334 | 1530.695403 | 46.363681 | 50.440498 | 54.98490143 |
| 15 | 1985 | YR1985 | El Salvador | SLV | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | .. |
| 16 | 1985 | YR1985 | Guatemala | GTM | 597.5586548 | 2121.873660 | 45.939359 | 51.010453 | 38.06546021 |
| 17 | 1985 | YR1985 | Honduras | HND | 868.4234009 | 1547.357836 | 46.190833 | 50.604823 | .. |
| 18 | 1986 | YR1986 | El Salvador | SLV | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | .. |
| 19 | 1986 | YR1986 | Guatemala | GTM | 626.2062378 | 2073.066614 | 45.963895 | 50.941242 | 41.00183868 |
| 20 | 1986 | YR1986 | Honduras | HND | .. | 1512.507552 | 46.064093 | 50.698642 | .. |
| 21 | 1987 | YR1987 | El Salvador | SLV | .. | 2079.844180 | 41.318260 | 54.619889 | 61.98265839 |
| 22 | 1987 | YR1987 | Guatemala | GTM | .. | 2095.342199 | 45.884100 | 50.970232 | .. |
| 23 | 1987 | YR1987 | Honduras | HND | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | .. |
| 24 | 1988 | YR1988 | El Salvador | SLV | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.72280121 |
| 25 | 1988 | YR1988 | Guatemala | GTM | .. | 2125.624163 | 45.729366 | 51.069993 | .. |
| 26 | 1988 | YR1988 | Honduras | HND | 930.5723877 | 1581.639092 | 45.800132 | 50.875323 | .. |
| 27 | 1989 | YR1989 | El Salvador | SLV | 1568.25354 | 2084.671422 | 40.362708 | 55.377693 | 63.61275101 |
| 28 | 1989 | YR1989 | Guatemala | GTM | .. | 2157.313890 | 45.551915 | 51.191567 | .. |
| 29 | 1989 | YR1989 | Honduras | HND | 925.7636719 | 1603.219717 | 45.642320 | 50.989531 | .. |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | YR2006 | El Salvador | SLV | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.3642807 |
| 79 | 2006 | YR2006 | Guatemala | GTM | .. | 2698.985240 | 41.024086 | 54.692058 | 75.35977936 |
| 80 | 2006 | YR2006 | Honduras | HND | .. | 2017.943010 | 38.938629 | 56.863273 | 88.26262665 |
| 81 | 2007 | YR2007 | El Salvador | SLV | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.29842377 |
| 82 | 2007 | YR2007 | Guatemala | GTM | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.83374786 |
| 83 | 2007 | YR2007 | Honduras | HND | .. | 2104.759589 | 38.204492 | 57.552528 | .. |
| 84 | 2008 | YR2008 | El Salvador | SLV | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.28109741 |
| 85 | 2008 | YR2008 | Guatemala | GTM | .. | 2833.735795 | 40.091781 | 55.548046 | 78.54676056 |
| 86 | 2008 | YR2008 | Honduras | HND | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.76953888 |
| 87 | 2009 | YR2009 | El Salvador | SLV | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.6757813 |
| 88 | 2009 | YR2009 | Guatemala | GTM | .. | 2787.128287 | 39.593279 | 55.997451 | 82.20065308 |
| 89 | 2009 | YR2009 | Honduras | HND | .. | 2068.185180 | 36.665394 | 58.987298 | 91.86322784 |
| 90 | 2010 | YR2010 | El Salvador | SLV | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 |
| 91 | 2010 | YR2010 | Guatemala | GTM | .. | 2805.951416 | 39.095628 | 56.434019 | 84.21375275 |
| 92 | 2010 | YR2010 | Honduras | HND | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.18988037 |
| 93 | 2011 | YR2011 | El Salvador | SLV | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.1046219 |
| 94 | 2011 | YR2011 | Guatemala | GTM | .. | 2861.167894 | 38.577533 | 56.887778 | 86.68910217 |
| 95 | 2011 | YR2011 | Honduras | HND | .. | 2157.984444 | 35.042579 | 60.480535 | 100.7206421 |
| 96 | 2012 | YR2012 | El Salvador | SLV | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.7987289 |
| 97 | 2012 | YR2012 | Guatemala | GTM | .. | 2884.897429 | 38.086602 | 57.307763 | 86.08334351 |
| 98 | 2012 | YR2012 | Honduras | HND | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.6761017 |
| 99 | 2013 | YR2013 | El Salvador | SLV | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.8399887 |
| 100 | 2013 | YR2013 | Guatemala | GTM | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.50177002 |
| 101 | 2013 | YR2013 | Honduras | HND | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.47953033 |
| 102 | 2014 | YR2014 | El Salvador | SLV | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.6170197 |
| 103 | 2014 | YR2014 | Guatemala | GTM | .. | 2990.594485 | 37.120959 | 58.115978 | 86.6244278 |
| 104 | 2014 | YR2014 | Honduras | HND | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.7219696 |
| 105 | 2015 | YR2015 | El Salvador | SLV | .. | 3853.107631 | 27.028606 | 64.799595 | .. |
| 106 | 2015 | YR2015 | Guatemala | GTM | .. | 3052.270569 | 36.622822 | 58.530645 | .. |
| 107 | 2015 | YR2015 | Honduras | HND | .. | 2329.002149 | 31.762798 | 63.383938 | .. |
108 rows × 9 columns
del mig2['Time Code']
del mig2['Country Code']
mig2
| Time | Country | Enrolment in tertiary education per 100,000 inhabitants, both sexes [UIS.TE_100000.56] | GDP per capita (constant 2005 US$) [NY.GDP.PCAP.KD] | Population, ages 0-14 (% of total) [SP.POP.0014.TO.ZS] | Population, ages 15-64 (% of total) [SP.POP.1564.TO.ZS] | Primary completion rate, both sexes (%) [SE.PRM.CMPT.ZS] | |
|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | .. | 2572.813235 | 43.742478 | 52.756733 | .. |
| 1 | 1980 | Guatemala | .. | 2560.782037 | 45.444923 | 51.602977 | 33.9041481 |
| 2 | 1980 | Honduras | 713.5259399 | 1655.946421 | 46.957200 | 49.818337 | 44.57500076 |
| 3 | 1981 | El Salvador | .. | 2267.095959 | 43.481122 | 52.948845 | 46.45079041 |
| 4 | 1981 | Guatemala | 493.2778625 | 2509.736778 | 45.617358 | 51.409643 | 33.95742035 |
| 5 | 1981 | Honduras | 821.0927124 | 1645.846419 | 46.892259 | 49.886066 | .. |
| 6 | 1982 | El Salvador | 999.5952759 | 2092.554425 | 43.204606 | 53.154795 | 49.3827095 |
| 7 | 1982 | Guatemala | .. | 2357.368296 | 45.771834 | 51.239138 | 33.8807106 |
| 8 | 1982 | Honduras | 864.4645996 | 1573.671559 | 46.745647 | 50.043171 | 49.94493866 |
| 9 | 1983 | El Salvador | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.6135788 |
| 10 | 1983 | Guatemala | 571.4475708 | 2236.567544 | 45.891347 | 51.104597 | 35.13737869 |
| 11 | 1983 | Honduras | 868.4456787 | 1512.185833 | 46.554395 | 50.246028 | .. |
| 12 | 1984 | El Salvador | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.6973114 |
| 13 | 1984 | Guatemala | 578.3195801 | 2189.829730 | 45.951383 | 51.025182 | 36.57794952 |
| 14 | 1984 | Honduras | 874.7142334 | 1530.695403 | 46.363681 | 50.440498 | 54.98490143 |
| 15 | 1985 | El Salvador | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | .. |
| 16 | 1985 | Guatemala | 597.5586548 | 2121.873660 | 45.939359 | 51.010453 | 38.06546021 |
| 17 | 1985 | Honduras | 868.4234009 | 1547.357836 | 46.190833 | 50.604823 | .. |
| 18 | 1986 | El Salvador | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | .. |
| 19 | 1986 | Guatemala | 626.2062378 | 2073.066614 | 45.963895 | 50.941242 | 41.00183868 |
| 20 | 1986 | Honduras | .. | 1512.507552 | 46.064093 | 50.698642 | .. |
| 21 | 1987 | El Salvador | .. | 2079.844180 | 41.318260 | 54.619889 | 61.98265839 |
| 22 | 1987 | Guatemala | .. | 2095.342199 | 45.884100 | 50.970232 | .. |
| 23 | 1987 | Honduras | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | .. |
| 24 | 1988 | El Salvador | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.72280121 |
| 25 | 1988 | Guatemala | .. | 2125.624163 | 45.729366 | 51.069993 | .. |
| 26 | 1988 | Honduras | 930.5723877 | 1581.639092 | 45.800132 | 50.875323 | .. |
| 27 | 1989 | El Salvador | 1568.25354 | 2084.671422 | 40.362708 | 55.377693 | 63.61275101 |
| 28 | 1989 | Guatemala | .. | 2157.313890 | 45.551915 | 51.191567 | .. |
| 29 | 1989 | Honduras | 925.7636719 | 1603.219717 | 45.642320 | 50.989531 | .. |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.3642807 |
| 79 | 2006 | Guatemala | .. | 2698.985240 | 41.024086 | 54.692058 | 75.35977936 |
| 80 | 2006 | Honduras | .. | 2017.943010 | 38.938629 | 56.863273 | 88.26262665 |
| 81 | 2007 | El Salvador | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.29842377 |
| 82 | 2007 | Guatemala | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.83374786 |
| 83 | 2007 | Honduras | .. | 2104.759589 | 38.204492 | 57.552528 | .. |
| 84 | 2008 | El Salvador | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.28109741 |
| 85 | 2008 | Guatemala | .. | 2833.735795 | 40.091781 | 55.548046 | 78.54676056 |
| 86 | 2008 | Honduras | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.76953888 |
| 87 | 2009 | El Salvador | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.6757813 |
| 88 | 2009 | Guatemala | .. | 2787.128287 | 39.593279 | 55.997451 | 82.20065308 |
| 89 | 2009 | Honduras | .. | 2068.185180 | 36.665394 | 58.987298 | 91.86322784 |
| 90 | 2010 | El Salvador | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 |
| 91 | 2010 | Guatemala | .. | 2805.951416 | 39.095628 | 56.434019 | 84.21375275 |
| 92 | 2010 | Honduras | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.18988037 |
| 93 | 2011 | El Salvador | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.1046219 |
| 94 | 2011 | Guatemala | .. | 2861.167894 | 38.577533 | 56.887778 | 86.68910217 |
| 95 | 2011 | Honduras | .. | 2157.984444 | 35.042579 | 60.480535 | 100.7206421 |
| 96 | 2012 | El Salvador | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.7987289 |
| 97 | 2012 | Guatemala | .. | 2884.897429 | 38.086602 | 57.307763 | 86.08334351 |
| 98 | 2012 | Honduras | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.6761017 |
| 99 | 2013 | El Salvador | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.8399887 |
| 100 | 2013 | Guatemala | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.50177002 |
| 101 | 2013 | Honduras | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.47953033 |
| 102 | 2014 | El Salvador | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.6170197 |
| 103 | 2014 | Guatemala | .. | 2990.594485 | 37.120959 | 58.115978 | 86.6244278 |
| 104 | 2014 | Honduras | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.7219696 |
| 105 | 2015 | El Salvador | .. | 3853.107631 | 27.028606 | 64.799595 | .. |
| 106 | 2015 | Guatemala | .. | 3052.270569 | 36.622822 | 58.530645 | .. |
| 107 | 2015 | Honduras | .. | 2329.002149 | 31.762798 | 63.383938 | .. |
108 rows × 7 columns
mig2.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 7 columns): Time 108 non-null int64 Country 108 non-null object Enrolment in tertiary education per 100,000 inhabitants, both sexes [UIS.TE_100000.56] 108 non-null object GDP per capita (constant 2005 US$) [NY.GDP.PCAP.KD] 108 non-null float64 Population, ages 0-14 (% of total) [SP.POP.0014.TO.ZS] 108 non-null float64 Population, ages 15-64 (% of total) [SP.POP.1564.TO.ZS] 108 non-null float64 Primary completion rate, both sexes (%) [SE.PRM.CMPT.ZS] 108 non-null object dtypes: float64(3), int64(1), object(3) memory usage: 6.0+ KB
mig2.rename(columns={
'Time': 'Year',
'Enrolment in tertiary education per 100,000 inhabitants, both sexes [UIS.TE_100000.56]': 'enrolment_tertiary',
'GDP per capita (constant 2005 US$) [NY.GDP.PCAP.KD]': 'GDP_percapita_constant',
'Population, ages 0-14 (% of total) [SP.POP.0014.TO.ZS]': 'pop_ages_0-14%',
'Population, ages 15-64 (% of total) [SP.POP.1564.TO.ZS]': 'pop_ages_14-64%',
'Primary completion rate, both sexes (%) [SE.PRM.CMPT.ZS]': 'primary_completion'
}, inplace=True)
mig2
| Year | Country | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | |
|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | .. | 2572.813235 | 43.742478 | 52.756733 | .. |
| 1 | 1980 | Guatemala | .. | 2560.782037 | 45.444923 | 51.602977 | 33.9041481 |
| 2 | 1980 | Honduras | 713.5259399 | 1655.946421 | 46.957200 | 49.818337 | 44.57500076 |
| 3 | 1981 | El Salvador | .. | 2267.095959 | 43.481122 | 52.948845 | 46.45079041 |
| 4 | 1981 | Guatemala | 493.2778625 | 2509.736778 | 45.617358 | 51.409643 | 33.95742035 |
| 5 | 1981 | Honduras | 821.0927124 | 1645.846419 | 46.892259 | 49.886066 | .. |
| 6 | 1982 | El Salvador | 999.5952759 | 2092.554425 | 43.204606 | 53.154795 | 49.3827095 |
| 7 | 1982 | Guatemala | .. | 2357.368296 | 45.771834 | 51.239138 | 33.8807106 |
| 8 | 1982 | Honduras | 864.4645996 | 1573.671559 | 46.745647 | 50.043171 | 49.94493866 |
| 9 | 1983 | El Salvador | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.6135788 |
| 10 | 1983 | Guatemala | 571.4475708 | 2236.567544 | 45.891347 | 51.104597 | 35.13737869 |
| 11 | 1983 | Honduras | 868.4456787 | 1512.185833 | 46.554395 | 50.246028 | .. |
| 12 | 1984 | El Salvador | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.6973114 |
| 13 | 1984 | Guatemala | 578.3195801 | 2189.829730 | 45.951383 | 51.025182 | 36.57794952 |
| 14 | 1984 | Honduras | 874.7142334 | 1530.695403 | 46.363681 | 50.440498 | 54.98490143 |
| 15 | 1985 | El Salvador | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | .. |
| 16 | 1985 | Guatemala | 597.5586548 | 2121.873660 | 45.939359 | 51.010453 | 38.06546021 |
| 17 | 1985 | Honduras | 868.4234009 | 1547.357836 | 46.190833 | 50.604823 | .. |
| 18 | 1986 | El Salvador | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | .. |
| 19 | 1986 | Guatemala | 626.2062378 | 2073.066614 | 45.963895 | 50.941242 | 41.00183868 |
| 20 | 1986 | Honduras | .. | 1512.507552 | 46.064093 | 50.698642 | .. |
| 21 | 1987 | El Salvador | .. | 2079.844180 | 41.318260 | 54.619889 | 61.98265839 |
| 22 | 1987 | Guatemala | .. | 2095.342199 | 45.884100 | 50.970232 | .. |
| 23 | 1987 | Honduras | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | .. |
| 24 | 1988 | El Salvador | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.72280121 |
| 25 | 1988 | Guatemala | .. | 2125.624163 | 45.729366 | 51.069993 | .. |
| 26 | 1988 | Honduras | 930.5723877 | 1581.639092 | 45.800132 | 50.875323 | .. |
| 27 | 1989 | El Salvador | 1568.25354 | 2084.671422 | 40.362708 | 55.377693 | 63.61275101 |
| 28 | 1989 | Guatemala | .. | 2157.313890 | 45.551915 | 51.191567 | .. |
| 29 | 1989 | Honduras | 925.7636719 | 1603.219717 | 45.642320 | 50.989531 | .. |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.3642807 |
| 79 | 2006 | Guatemala | .. | 2698.985240 | 41.024086 | 54.692058 | 75.35977936 |
| 80 | 2006 | Honduras | .. | 2017.943010 | 38.938629 | 56.863273 | 88.26262665 |
| 81 | 2007 | El Salvador | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.29842377 |
| 82 | 2007 | Guatemala | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.83374786 |
| 83 | 2007 | Honduras | .. | 2104.759589 | 38.204492 | 57.552528 | .. |
| 84 | 2008 | El Salvador | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.28109741 |
| 85 | 2008 | Guatemala | .. | 2833.735795 | 40.091781 | 55.548046 | 78.54676056 |
| 86 | 2008 | Honduras | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.76953888 |
| 87 | 2009 | El Salvador | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.6757813 |
| 88 | 2009 | Guatemala | .. | 2787.128287 | 39.593279 | 55.997451 | 82.20065308 |
| 89 | 2009 | Honduras | .. | 2068.185180 | 36.665394 | 58.987298 | 91.86322784 |
| 90 | 2010 | El Salvador | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 |
| 91 | 2010 | Guatemala | .. | 2805.951416 | 39.095628 | 56.434019 | 84.21375275 |
| 92 | 2010 | Honduras | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.18988037 |
| 93 | 2011 | El Salvador | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.1046219 |
| 94 | 2011 | Guatemala | .. | 2861.167894 | 38.577533 | 56.887778 | 86.68910217 |
| 95 | 2011 | Honduras | .. | 2157.984444 | 35.042579 | 60.480535 | 100.7206421 |
| 96 | 2012 | El Salvador | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.7987289 |
| 97 | 2012 | Guatemala | .. | 2884.897429 | 38.086602 | 57.307763 | 86.08334351 |
| 98 | 2012 | Honduras | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.6761017 |
| 99 | 2013 | El Salvador | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.8399887 |
| 100 | 2013 | Guatemala | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.50177002 |
| 101 | 2013 | Honduras | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.47953033 |
| 102 | 2014 | El Salvador | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.6170197 |
| 103 | 2014 | Guatemala | .. | 2990.594485 | 37.120959 | 58.115978 | 86.6244278 |
| 104 | 2014 | Honduras | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.7219696 |
| 105 | 2015 | El Salvador | .. | 3853.107631 | 27.028606 | 64.799595 | .. |
| 106 | 2015 | Guatemala | .. | 3052.270569 | 36.622822 | 58.530645 | .. |
| 107 | 2015 | Honduras | .. | 2329.002149 | 31.762798 | 63.383938 | .. |
108 rows × 7 columns
mig2['primary_completion'] = pd.to_numeric(mig2['primary_completion'], errors='coerce')
mig2['enrolment_tertiary'] = pd.to_numeric(mig2['enrolment_tertiary'], errors='coerce')
mig2
| Year | Country | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | |
|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | NaN | 2572.813235 | 43.742478 | 52.756733 | NaN |
| 1 | 1980 | Guatemala | NaN | 2560.782037 | 45.444923 | 51.602977 | 33.904148 |
| 2 | 1980 | Honduras | 713.525940 | 1655.946421 | 46.957200 | 49.818337 | 44.575001 |
| 3 | 1981 | El Salvador | NaN | 2267.095959 | 43.481122 | 52.948845 | 46.450790 |
| 4 | 1981 | Guatemala | 493.277863 | 2509.736778 | 45.617358 | 51.409643 | 33.957420 |
| 5 | 1981 | Honduras | 821.092712 | 1645.846419 | 46.892259 | 49.886066 | NaN |
| 6 | 1982 | El Salvador | 999.595276 | 2092.554425 | 43.204606 | 53.154795 | 49.382709 |
| 7 | 1982 | Guatemala | NaN | 2357.368296 | 45.771834 | 51.239138 | 33.880711 |
| 8 | 1982 | Honduras | 864.464600 | 1573.671559 | 46.745647 | 50.043171 | 49.944939 |
| 9 | 1983 | El Salvador | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.613579 |
| 10 | 1983 | Guatemala | 571.447571 | 2236.567544 | 45.891347 | 51.104597 | 35.137379 |
| 11 | 1983 | Honduras | 868.445679 | 1512.185833 | 46.554395 | 50.246028 | NaN |
| 12 | 1984 | El Salvador | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.697311 |
| 13 | 1984 | Guatemala | 578.319580 | 2189.829730 | 45.951383 | 51.025182 | 36.577950 |
| 14 | 1984 | Honduras | 874.714233 | 1530.695403 | 46.363681 | 50.440498 | 54.984901 |
| 15 | 1985 | El Salvador | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | NaN |
| 16 | 1985 | Guatemala | 597.558655 | 2121.873660 | 45.939359 | 51.010453 | 38.065460 |
| 17 | 1985 | Honduras | 868.423401 | 1547.357836 | 46.190833 | 50.604823 | NaN |
| 18 | 1986 | El Salvador | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | NaN |
| 19 | 1986 | Guatemala | 626.206238 | 2073.066614 | 45.963895 | 50.941242 | 41.001839 |
| 20 | 1986 | Honduras | NaN | 1512.507552 | 46.064093 | 50.698642 | NaN |
| 21 | 1987 | El Salvador | NaN | 2079.844180 | 41.318260 | 54.619889 | 61.982658 |
| 22 | 1987 | Guatemala | NaN | 2095.342199 | 45.884100 | 50.970232 | NaN |
| 23 | 1987 | Honduras | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | NaN |
| 24 | 1988 | El Salvador | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.722801 |
| 25 | 1988 | Guatemala | NaN | 2125.624163 | 45.729366 | 51.069993 | NaN |
| 26 | 1988 | Honduras | 930.572388 | 1581.639092 | 45.800132 | 50.875323 | NaN |
| 27 | 1989 | El Salvador | 1568.253540 | 2084.671422 | 40.362708 | 55.377693 | 63.612751 |
| 28 | 1989 | Guatemala | NaN | 2157.313890 | 45.551915 | 51.191567 | NaN |
| 29 | 1989 | Honduras | 925.763672 | 1603.219717 | 45.642320 | 50.989531 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.364281 |
| 79 | 2006 | Guatemala | NaN | 2698.985240 | 41.024086 | 54.692058 | 75.359779 |
| 80 | 2006 | Honduras | NaN | 2017.943010 | 38.938629 | 56.863273 | 88.262627 |
| 81 | 2007 | El Salvador | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.298424 |
| 82 | 2007 | Guatemala | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.833748 |
| 83 | 2007 | Honduras | NaN | 2104.759589 | 38.204492 | 57.552528 | NaN |
| 84 | 2008 | El Salvador | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.281097 |
| 85 | 2008 | Guatemala | NaN | 2833.735795 | 40.091781 | 55.548046 | 78.546761 |
| 86 | 2008 | Honduras | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.769539 |
| 87 | 2009 | El Salvador | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.675781 |
| 88 | 2009 | Guatemala | NaN | 2787.128287 | 39.593279 | 55.997451 | 82.200653 |
| 89 | 2009 | Honduras | NaN | 2068.185180 | 36.665394 | 58.987298 | 91.863228 |
| 90 | 2010 | El Salvador | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 |
| 91 | 2010 | Guatemala | NaN | 2805.951416 | 39.095628 | 56.434019 | 84.213753 |
| 92 | 2010 | Honduras | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.189880 |
| 93 | 2011 | El Salvador | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.104622 |
| 94 | 2011 | Guatemala | NaN | 2861.167894 | 38.577533 | 56.887778 | 86.689102 |
| 95 | 2011 | Honduras | NaN | 2157.984444 | 35.042579 | 60.480535 | 100.720642 |
| 96 | 2012 | El Salvador | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.798729 |
| 97 | 2012 | Guatemala | NaN | 2884.897429 | 38.086602 | 57.307763 | 86.083344 |
| 98 | 2012 | Honduras | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.676102 |
| 99 | 2013 | El Salvador | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.839989 |
| 100 | 2013 | Guatemala | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.501770 |
| 101 | 2013 | Honduras | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.479530 |
| 102 | 2014 | El Salvador | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.617020 |
| 103 | 2014 | Guatemala | NaN | 2990.594485 | 37.120959 | 58.115978 | 86.624428 |
| 104 | 2014 | Honduras | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.721970 |
| 105 | 2015 | El Salvador | NaN | 3853.107631 | 27.028606 | 64.799595 | NaN |
| 106 | 2015 | Guatemala | NaN | 3052.270569 | 36.622822 | 58.530645 | NaN |
| 107 | 2015 | Honduras | NaN | 2329.002149 | 31.762798 | 63.383938 | NaN |
108 rows × 7 columns
mig2.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 7 columns): Year 108 non-null int64 Country 108 non-null object enrolment_tertiary 68 non-null float64 GDP_percapita_constant 108 non-null float64 pop_ages_0-14% 108 non-null float64 pop_ages_14-64% 108 non-null float64 primary_completion 71 non-null float64 dtypes: float64(5), int64(1), object(1) memory usage: 6.0+ KB
mig2['enrolment_tertiary'].fillna(np.mean(mig2['enrolment_tertiary']), inplace=True)
mig2['primary_completion'].fillna(np.mean(mig2['primary_completion']), inplace=True)
mig2
| Year | Country | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | |
|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 1516.400016 | 2572.813235 | 43.742478 | 52.756733 | 73.720787 |
| 1 | 1980 | Guatemala | 1516.400016 | 2560.782037 | 45.444923 | 51.602977 | 33.904148 |
| 2 | 1980 | Honduras | 713.525940 | 1655.946421 | 46.957200 | 49.818337 | 44.575001 |
| 3 | 1981 | El Salvador | 1516.400016 | 2267.095959 | 43.481122 | 52.948845 | 46.450790 |
| 4 | 1981 | Guatemala | 493.277863 | 2509.736778 | 45.617358 | 51.409643 | 33.957420 |
| 5 | 1981 | Honduras | 821.092712 | 1645.846419 | 46.892259 | 49.886066 | 73.720787 |
| 6 | 1982 | El Salvador | 999.595276 | 2092.554425 | 43.204606 | 53.154795 | 49.382709 |
| 7 | 1982 | Guatemala | 1516.400016 | 2357.368296 | 45.771834 | 51.239138 | 33.880711 |
| 8 | 1982 | Honduras | 864.464600 | 1573.671559 | 46.745647 | 50.043171 | 49.944939 |
| 9 | 1983 | El Salvador | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.613579 |
| 10 | 1983 | Guatemala | 571.447571 | 2236.567544 | 45.891347 | 51.104597 | 35.137379 |
| 11 | 1983 | Honduras | 868.445679 | 1512.185833 | 46.554395 | 50.246028 | 73.720787 |
| 12 | 1984 | El Salvador | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.697311 |
| 13 | 1984 | Guatemala | 578.319580 | 2189.829730 | 45.951383 | 51.025182 | 36.577950 |
| 14 | 1984 | Honduras | 874.714233 | 1530.695403 | 46.363681 | 50.440498 | 54.984901 |
| 15 | 1985 | El Salvador | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | 73.720787 |
| 16 | 1985 | Guatemala | 597.558655 | 2121.873660 | 45.939359 | 51.010453 | 38.065460 |
| 17 | 1985 | Honduras | 868.423401 | 1547.357836 | 46.190833 | 50.604823 | 73.720787 |
| 18 | 1986 | El Salvador | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | 73.720787 |
| 19 | 1986 | Guatemala | 626.206238 | 2073.066614 | 45.963895 | 50.941242 | 41.001839 |
| 20 | 1986 | Honduras | 1516.400016 | 1512.507552 | 46.064093 | 50.698642 | 73.720787 |
| 21 | 1987 | El Salvador | 1516.400016 | 2079.844180 | 41.318260 | 54.619889 | 61.982658 |
| 22 | 1987 | Guatemala | 1516.400016 | 2095.342199 | 45.884100 | 50.970232 | 73.720787 |
| 23 | 1987 | Honduras | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | 73.720787 |
| 24 | 1988 | El Salvador | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.722801 |
| 25 | 1988 | Guatemala | 1516.400016 | 2125.624163 | 45.729366 | 51.069993 | 73.720787 |
| 26 | 1988 | Honduras | 930.572388 | 1581.639092 | 45.800132 | 50.875323 | 73.720787 |
| 27 | 1989 | El Salvador | 1568.253540 | 2084.671422 | 40.362708 | 55.377693 | 63.612751 |
| 28 | 1989 | Guatemala | 1516.400016 | 2157.313890 | 45.551915 | 51.191567 | 73.720787 |
| 29 | 1989 | Honduras | 925.763672 | 1603.219717 | 45.642320 | 50.989531 | 73.720787 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.364281 |
| 79 | 2006 | Guatemala | 1516.400016 | 2698.985240 | 41.024086 | 54.692058 | 75.359779 |
| 80 | 2006 | Honduras | 1516.400016 | 2017.943010 | 38.938629 | 56.863273 | 88.262627 |
| 81 | 2007 | El Salvador | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.298424 |
| 82 | 2007 | Guatemala | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.833748 |
| 83 | 2007 | Honduras | 1516.400016 | 2104.759589 | 38.204492 | 57.552528 | 73.720787 |
| 84 | 2008 | El Salvador | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.281097 |
| 85 | 2008 | Guatemala | 1516.400016 | 2833.735795 | 40.091781 | 55.548046 | 78.546761 |
| 86 | 2008 | Honduras | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.769539 |
| 87 | 2009 | El Salvador | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.675781 |
| 88 | 2009 | Guatemala | 1516.400016 | 2787.128287 | 39.593279 | 55.997451 | 82.200653 |
| 89 | 2009 | Honduras | 1516.400016 | 2068.185180 | 36.665394 | 58.987298 | 91.863228 |
| 90 | 2010 | El Salvador | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 |
| 91 | 2010 | Guatemala | 1516.400016 | 2805.951416 | 39.095628 | 56.434019 | 84.213753 |
| 92 | 2010 | Honduras | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.189880 |
| 93 | 2011 | El Salvador | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.104622 |
| 94 | 2011 | Guatemala | 1516.400016 | 2861.167894 | 38.577533 | 56.887778 | 86.689102 |
| 95 | 2011 | Honduras | 1516.400016 | 2157.984444 | 35.042579 | 60.480535 | 100.720642 |
| 96 | 2012 | El Salvador | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.798729 |
| 97 | 2012 | Guatemala | 1516.400016 | 2884.897429 | 38.086602 | 57.307763 | 86.083344 |
| 98 | 2012 | Honduras | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.676102 |
| 99 | 2013 | El Salvador | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.839989 |
| 100 | 2013 | Guatemala | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.501770 |
| 101 | 2013 | Honduras | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.479530 |
| 102 | 2014 | El Salvador | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.617020 |
| 103 | 2014 | Guatemala | 1516.400016 | 2990.594485 | 37.120959 | 58.115978 | 86.624428 |
| 104 | 2014 | Honduras | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.721970 |
| 105 | 2015 | El Salvador | 1516.400016 | 3853.107631 | 27.028606 | 64.799595 | 73.720787 |
| 106 | 2015 | Guatemala | 1516.400016 | 3052.270569 | 36.622822 | 58.530645 | 73.720787 |
| 107 | 2015 | Honduras | 1516.400016 | 2329.002149 | 31.762798 | 63.383938 | 73.720787 |
108 rows × 7 columns
mig3
| Year | Year Code | Country | Country Code | GINI index (World Bank estimate) [SI.POV.GINI] | Income share held by fourth 20% [SI.DST.04TH.20] | Income share held by highest 10% [SI.DST.10TH.10] | Income share held by highest 20% [SI.DST.05TH.20] | Income share held by lowest 10% [SI.DST.FRST.10] | Income share held by lowest 20% [SI.DST.FRST.20] | Income share held by second 20% [SI.DST.02ND.20] | Income share held by third 20% [SI.DST.03RD.20] | Number of poor at $1.90 a day (2011 PPP) (millions) [SI.POV.NOP1] | Number of poor at $3.10 a day (2011 PPP) (millions) [SI.POV.NOP2] | Poverty gap at $1.90 a day (2011 PPP) (%) [SI.POV.GAPS] | Poverty gap at $3.10 a day (2011 PPP) (%) [SI.POV.GAP2] | Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [SI.POV.DDAY] | Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population) [SI.POV.2DAY] | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | YR1980 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 1 | 1980 | YR1980 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 2 | 1980 | YR1980 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 3 | 1981 | YR1981 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 4 | 1981 | YR1981 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 5 | 1981 | YR1981 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 6 | 1982 | YR1982 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 7 | 1982 | YR1982 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 8 | 1982 | YR1982 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 9 | 1983 | YR1983 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 10 | 1983 | YR1983 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 11 | 1983 | YR1983 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 12 | 1984 | YR1984 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 13 | 1984 | YR1984 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 14 | 1984 | YR1984 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 15 | 1985 | YR1985 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 16 | 1985 | YR1985 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 17 | 1985 | YR1985 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 18 | 1986 | YR1986 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 19 | 1986 | YR1986 | Guatemala | GTM | 58.26 | 18.35 | 46.73 | 61.96 | 1 | 2.77 | 6.18 | 10.75 | 4.238208 | 5.79488 | 24.99 | 39.01 | 50.94 | 69.65 |
| 20 | 1986 | YR1986 | Honduras | HND | 55.09 | 19.35 | 43.26 | 59.41 | 1.23 | 3.23 | 6.67 | 11.36 | 0.422176 | 0.713758 | 9.15 | 18.94 | 25.28 | 42.74 |
| 21 | 1987 | YR1987 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 22 | 1987 | YR1987 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 23 | 1987 | YR1987 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 24 | 1988 | YR1988 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 25 | 1988 | YR1988 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 26 | 1988 | YR1988 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 27 | 1989 | YR1989 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | 0.983164 | 1.632736 | 11.24 | 16.66 | 18.98 | 31.52 |
| 28 | 1989 | YR1989 | Guatemala | GTM | 59.6 | 18.78 | 46.78 | 62.87 | 0.68 | 2.15 | 5.68 | 10.53 | 3.398988 | 4.919682 | 18.71 | 29.68 | 38.02 | 55.03 |
| 29 | 1989 | YR1989 | Honduras | HND | 59.49 | 17.82 | 48.18 | 63.31 | 1.04 | 2.76 | 5.87 | 10.24 | 1.84122 | 2.722716 | 16.9 | 29.14 | 38.6 | 57.08 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | YR2006 | El Salvador | SLV | 45.44 | 20.76 | 35.48 | 51.15 | 1.79 | 4.89 | 9.37 | 13.85 | 0.379692 | 1.035198 | 1.73 | 5.57 | 6.36 | 17.34 |
| 79 | 2006 | YR2006 | Guatemala | GTM | 54.89 | 19.09 | 43.56 | 59.17 | 1.07 | 3.13 | 6.99 | 11.63 | 1.552699 | 3.195781 | 3.93 | 9.27 | 11.51 | 23.69 |
| 80 | 2006 | YR2006 | Honduras | HND | 57.42 | 19.7 | 44.05 | 60.63 | 0.58 | 2.13 | 6.16 | 11.37 | 1.667679 | 2.61473 | 11.4 | 18.85 | 23.79 | 37.3 |
| 81 | 2007 | YR2007 | El Salvador | SLV | 45.24 | 20.51 | 35.72 | 51.24 | 1.93 | 5.18 | 9.41 | 13.66 | 0.268951 | 0.835006 | 1.08 | 4.1 | 4.49 | 13.94 |
| 82 | 2007 | YR2007 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 83 | 2007 | YR2007 | Honduras | HND | 56.16 | 19.18 | 43.81 | 60.3 | 0.9 | 2.81 | 6.59 | 11.12 | 1.242759 | 2.279461 | 6.91 | 13.88 | 17.43 | 31.97 |
| 84 | 2008 | YR2008 | El Salvador | SLV | 46.65 | 20.75 | 36.04 | 52.2 | 1.7 | 4.67 | 8.96 | 13.41 | 0.4152 | 1.1148 | 1.99 | 6.09 | 6.92 | 18.58 |
| 85 | 2008 | YR2008 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 86 | 2008 | YR2008 | Honduras | HND | 55.74 | 19.25 | 43.87 | 59.58 | 0.91 | 2.83 | 6.76 | 11.57 | 1.171764 | 2.13081 | 6.3 | 12.68 | 16.14 | 29.35 |
| 87 | 2009 | YR2009 | El Salvador | SLV | 45.93 | 20.44 | 36.07 | 51.74 | 1.78 | 4.85 | 9.21 | 13.75 | 0.384678 | 1.054102 | 1.67 | 5.52 | 6.39 | 17.51 |
| 88 | 2009 | YR2009 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 89 | 2009 | YR2009 | Honduras | HND | 51.56 | 20.65 | 39.14 | 55.81 | 1.15 | 3.35 | 7.54 | 12.65 | 1.036152 | 1.979316 | 4.82 | 10.88 | 14.04 | 26.82 |
| 90 | 2010 | YR2010 | El Salvador | SLV | 44.53 | 21.6 | 33.7 | 50 | 1.67 | 4.74 | 9.44 | 14.22 | 0.437296 | 1.12042 | 2.33 | 6.3 | 7.24 | 18.55 |
| 91 | 2010 | YR2010 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 92 | 2010 | YR2010 | Honduras | HND | 53.39 | 20.18 | 41.02 | 57.59 | 1.09 | 3.19 | 7.13 | 11.91 | 1.16025 | 2.18325 | 5.4 | 11.9 | 15.47 | 29.11 |
| 93 | 2011 | YR2011 | El Salvador | SLV | 42.43 | 21.26 | 32.86 | 48.74 | 2.11 | 5.57 | 10 | 14.44 | 0.274518 | 0.911424 | 1.06 | 4.39 | 4.53 | 15.04 |
| 94 | 2011 | YR2011 | Guatemala | GTM | 52.35 | 19.16 | 41.83 | 57.23 | 1.34 | 3.87 | 7.77 | 11.97 | 1.735265 | 3.983735 | 4 | 9.84 | 11.53 | 26.47 |
| 95 | 2011 | YR2011 | Honduras | HND | 57.4 | 18.56 | 45.67 | 61.23 | 0.75 | 2.61 | 6.54 | 11.08 | 1.42875 | 2.489454 | 7.88 | 14.66 | 18.75 | 32.67 |
| 96 | 2012 | YR2012 | El Salvador | SLV | 41.8 | 21.3 | 32.47 | 48.15 | 2.15 | 5.7 | 10.17 | 14.67 | 0.252512 | 0.826127 | 0.98 | 3.84 | 4.16 | 13.61 |
| 97 | 2012 | YR2012 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 98 | 2012 | YR2012 | Honduras | HND | 57.4 | 18.56 | 45.68 | 61.13 | 0.79 | 2.63 | 6.52 | 11.16 | 1.653264 | 2.883924 | 9.25 | 17.1 | 21.36 | 37.26 |
| 99 | 2013 | YR2013 | El Salvador | SLV | 43.51 | 20.68 | 34.35 | 49.79 | 2.11 | 5.52 | 9.82 | 14.2 | 0.197925 | 0.702177 | 0.74 | 3.16 | 3.25 | 11.53 |
| 100 | 2013 | YR2013 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 101 | 2013 | YR2013 | Honduras | HND | 53.67 | 19.94 | 41.48 | 57.66 | 0.98 | 3.1 | 7.2 | 12.1 | 1.486005 | 2.712175 | 7.66 | 15.24 | 18.93 | 34.55 |
| 102 | 2014 | YR2014 | El Salvador | SLV | 41.84 | 21.35 | 32.31 | 48.26 | 2.19 | 5.72 | 10.09 | 14.58 | 0.181467 | 0.689819 | 0.64 | 3 | 2.97 | 11.29 |
| 103 | 2014 | YR2014 | Guatemala | GTM | 48.66 | 20.06 | 38.36 | 53.91 | 1.64 | 4.44 | 8.5 | 13.08 | 1.493064 | 3.85281 | 2.72 | 8.11 | 9.32 | 24.05 |
| 104 | 2014 | YR2014 | Honduras | HND | 50.64 | 20.68 | 38.36 | 55.13 | 1.15 | 3.5 | 7.73 | 12.95 | 1.270416 | 2.484316 | 6.01 | 12.97 | 15.96 | 31.21 |
| 105 | 2015 | YR2015 | El Salvador | SLV | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 106 | 2015 | YR2015 | Guatemala | GTM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 107 | 2015 | YR2015 | Honduras | HND | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
108 rows × 18 columns
del mig3['Year Code']
del mig3['Country Code']
del mig3['Income share held by fourth 20% [SI.DST.04TH.20]']
del mig3['Income share held by highest 20% [SI.DST.05TH.20]']
del mig3['Income share held by lowest 20% [SI.DST.FRST.20]']
del mig3['Income share held by second 20% [SI.DST.02ND.20]']
del mig3['Income share held by third 20% [SI.DST.03RD.20]']
mig3.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 11 columns): Year 108 non-null int64 Country 108 non-null object GINI index (World Bank estimate) [SI.POV.GINI] 108 non-null object Income share held by highest 10% [SI.DST.10TH.10] 108 non-null object Income share held by lowest 10% [SI.DST.FRST.10] 108 non-null object Number of poor at $1.90 a day (2011 PPP) (millions) [SI.POV.NOP1] 108 non-null object Number of poor at $3.10 a day (2011 PPP) (millions) [SI.POV.NOP2] 108 non-null object Poverty gap at $1.90 a day (2011 PPP) (%) [SI.POV.GAPS] 108 non-null object Poverty gap at $3.10 a day (2011 PPP) (%) [SI.POV.GAP2] 108 non-null object Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [SI.POV.DDAY] 108 non-null object Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population) [SI.POV.2DAY] 108 non-null object dtypes: int64(1), object(10) memory usage: 9.4+ KB
mig3.rename(columns={
'GINI index (World Bank estimate) [SI.POV.GINI]': 'gini',
'Income share held by highest 10% [SI.DST.10TH.10]': 'income_highest%',
'Income share held by lowest 10% [SI.DST.FRST.10]': 'income_lowest%',
'Number of poor at $1.90 a day (2011 PPP) (millions) [SI.POV.NOP1]': 'poor_1.90',
'Number of poor at $3.10 a day (2011 PPP) (millions) [SI.POV.NOP2]': 'poor_3.10',
'Poverty gap at $1.90 a day (2011 PPP) (%) [SI.POV.GAPS]': 'poverty_gap_1.90',
'Poverty gap at $3.10 a day (2011 PPP) (%) [SI.POV.GAP2]': 'poverty_gap_3.10',
'Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [SI.POV.DDAY]': 'poverty_headcount_1.90',
'Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population) [SI.POV.2DAY]': 'poverty_headcount_3.10'
}, inplace=True)
mig3.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 11 columns): Year 108 non-null int64 Country 108 non-null object gini 108 non-null object income_highest% 108 non-null object income_lowest% 108 non-null object poor_1.90 108 non-null object poor_3.10 108 non-null object poverty_gap_1.90 108 non-null object poverty_gap_3.10 108 non-null object poverty_headcount_1.90 108 non-null object poverty_headcount_3.10 108 non-null object dtypes: int64(1), object(10) memory usage: 9.4+ KB
mig3['gini'] = pd.to_numeric(mig3['gini'], errors='coerce')
mig3['income_highest%'] = pd.to_numeric(mig3['income_highest%'], errors='coerce')
mig3['income_lowest%'] = pd.to_numeric(mig3['income_lowest%'], errors='coerce')
mig3['poor_1.90'] = pd.to_numeric(mig3['poor_1.90'], errors='coerce')
mig3['poor_3.10'] = pd.to_numeric(mig3['poor_3.10'], errors='coerce')
mig3['poverty_gap_1.90'] = pd.to_numeric(mig3['poverty_gap_1.90'], errors='coerce')
mig3['poverty_gap_3.10'] = pd.to_numeric(mig3['poverty_gap_3.10'], errors='coerce')
mig3['poverty_headcount_1.90'] = pd.to_numeric(mig3['poverty_headcount_1.90'], errors='coerce')
mig3['poverty_headcount_3.10'] = pd.to_numeric(mig3['poverty_headcount_3.10'], errors='coerce')
mig3
| Year | Country | gini | income_highest% | income_lowest% | poor_1.90 | poor_3.10 | poverty_gap_1.90 | poverty_gap_3.10 | poverty_headcount_1.90 | poverty_headcount_3.10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1980 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 1980 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 1981 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 1981 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 5 | 1981 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 6 | 1982 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7 | 1982 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 8 | 1982 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 9 | 1983 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10 | 1983 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 11 | 1983 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 12 | 1984 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13 | 1984 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 14 | 1984 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 15 | 1985 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 16 | 1985 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 17 | 1985 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 18 | 1986 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 19 | 1986 | Guatemala | 58.26 | 46.73 | 1.00 | 4.238208 | 5.794880 | 24.99 | 39.01 | 50.94 | 69.65 |
| 20 | 1986 | Honduras | 55.09 | 43.26 | 1.23 | 0.422176 | 0.713758 | 9.15 | 18.94 | 25.28 | 42.74 |
| 21 | 1987 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 22 | 1987 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 23 | 1987 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 24 | 1988 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 25 | 1988 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 26 | 1988 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 27 | 1989 | El Salvador | NaN | NaN | NaN | 0.983164 | 1.632736 | 11.24 | 16.66 | 18.98 | 31.52 |
| 28 | 1989 | Guatemala | 59.60 | 46.78 | 0.68 | 3.398988 | 4.919682 | 18.71 | 29.68 | 38.02 | 55.03 |
| 29 | 1989 | Honduras | 59.49 | 48.18 | 1.04 | 1.841220 | 2.722716 | 16.90 | 29.14 | 38.60 | 57.08 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 45.44 | 35.48 | 1.79 | 0.379692 | 1.035198 | 1.73 | 5.57 | 6.36 | 17.34 |
| 79 | 2006 | Guatemala | 54.89 | 43.56 | 1.07 | 1.552699 | 3.195781 | 3.93 | 9.27 | 11.51 | 23.69 |
| 80 | 2006 | Honduras | 57.42 | 44.05 | 0.58 | 1.667679 | 2.614730 | 11.40 | 18.85 | 23.79 | 37.30 |
| 81 | 2007 | El Salvador | 45.24 | 35.72 | 1.93 | 0.268951 | 0.835006 | 1.08 | 4.10 | 4.49 | 13.94 |
| 82 | 2007 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 83 | 2007 | Honduras | 56.16 | 43.81 | 0.90 | 1.242759 | 2.279461 | 6.91 | 13.88 | 17.43 | 31.97 |
| 84 | 2008 | El Salvador | 46.65 | 36.04 | 1.70 | 0.415200 | 1.114800 | 1.99 | 6.09 | 6.92 | 18.58 |
| 85 | 2008 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 86 | 2008 | Honduras | 55.74 | 43.87 | 0.91 | 1.171764 | 2.130810 | 6.30 | 12.68 | 16.14 | 29.35 |
| 87 | 2009 | El Salvador | 45.93 | 36.07 | 1.78 | 0.384678 | 1.054102 | 1.67 | 5.52 | 6.39 | 17.51 |
| 88 | 2009 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 89 | 2009 | Honduras | 51.56 | 39.14 | 1.15 | 1.036152 | 1.979316 | 4.82 | 10.88 | 14.04 | 26.82 |
| 90 | 2010 | El Salvador | 44.53 | 33.70 | 1.67 | 0.437296 | 1.120420 | 2.33 | 6.30 | 7.24 | 18.55 |
| 91 | 2010 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 92 | 2010 | Honduras | 53.39 | 41.02 | 1.09 | 1.160250 | 2.183250 | 5.40 | 11.90 | 15.47 | 29.11 |
| 93 | 2011 | El Salvador | 42.43 | 32.86 | 2.11 | 0.274518 | 0.911424 | 1.06 | 4.39 | 4.53 | 15.04 |
| 94 | 2011 | Guatemala | 52.35 | 41.83 | 1.34 | 1.735265 | 3.983735 | 4.00 | 9.84 | 11.53 | 26.47 |
| 95 | 2011 | Honduras | 57.40 | 45.67 | 0.75 | 1.428750 | 2.489454 | 7.88 | 14.66 | 18.75 | 32.67 |
| 96 | 2012 | El Salvador | 41.80 | 32.47 | 2.15 | 0.252512 | 0.826127 | 0.98 | 3.84 | 4.16 | 13.61 |
| 97 | 2012 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 98 | 2012 | Honduras | 57.40 | 45.68 | 0.79 | 1.653264 | 2.883924 | 9.25 | 17.10 | 21.36 | 37.26 |
| 99 | 2013 | El Salvador | 43.51 | 34.35 | 2.11 | 0.197925 | 0.702177 | 0.74 | 3.16 | 3.25 | 11.53 |
| 100 | 2013 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 101 | 2013 | Honduras | 53.67 | 41.48 | 0.98 | 1.486005 | 2.712175 | 7.66 | 15.24 | 18.93 | 34.55 |
| 102 | 2014 | El Salvador | 41.84 | 32.31 | 2.19 | 0.181467 | 0.689819 | 0.64 | 3.00 | 2.97 | 11.29 |
| 103 | 2014 | Guatemala | 48.66 | 38.36 | 1.64 | 1.493064 | 3.852810 | 2.72 | 8.11 | 9.32 | 24.05 |
| 104 | 2014 | Honduras | 50.64 | 38.36 | 1.15 | 1.270416 | 2.484316 | 6.01 | 12.97 | 15.96 | 31.21 |
| 105 | 2015 | El Salvador | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 106 | 2015 | Guatemala | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 107 | 2015 | Honduras | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
108 rows × 11 columns
mig3['gini'].fillna(np.mean(mig3['gini']), inplace=True)
mig3['income_highest%'].fillna(np.mean(mig3['income_highest%']), inplace=True)
mig3['income_lowest%'].fillna(np.mean(mig3['income_lowest%']), inplace=True)
mig3['poor_1.90'].fillna(np.mean(mig3['poor_1.90']), inplace=True)
mig3['poor_3.10'].fillna(np.mean(mig3['poor_3.10']), inplace=True)
mig3['poverty_gap_1.90'].fillna(np.mean(mig3['poverty_gap_1.90']), inplace=True)
mig3['poverty_gap_3.10'].fillna(np.mean(mig3['poverty_gap_3.10']), inplace=True)
mig3['poverty_headcount_1.90'].fillna(np.mean(mig3['poverty_headcount_1.90']), inplace=True)
mig3['poverty_headcount_3.10'].fillna(np.mean(mig3['poverty_headcount_3.10']), inplace=True)
mig3
| Year | Country | gini | income_highest% | income_lowest% | poor_1.90 | poor_3.10 | poverty_gap_1.90 | poverty_gap_3.10 | poverty_headcount_1.90 | poverty_headcount_3.10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 1 | 1980 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 2 | 1980 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 3 | 1981 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 4 | 1981 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 5 | 1981 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 6 | 1982 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 7 | 1982 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 8 | 1982 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 9 | 1983 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 10 | 1983 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 11 | 1983 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 12 | 1984 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 13 | 1984 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 14 | 1984 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 15 | 1985 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 16 | 1985 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 17 | 1985 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 18 | 1986 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 19 | 1986 | Guatemala | 58.260000 | 46.730000 | 1.000000 | 4.238208 | 5.794880 | 24.99 | 39.010 | 50.940000 | 69.650000 |
| 20 | 1986 | Honduras | 55.090000 | 43.260000 | 1.230000 | 0.422176 | 0.713758 | 9.15 | 18.940 | 25.280000 | 42.740000 |
| 21 | 1987 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 22 | 1987 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 23 | 1987 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 24 | 1988 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 25 | 1988 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 26 | 1988 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 27 | 1989 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 0.983164 | 1.632736 | 11.24 | 16.660 | 18.980000 | 31.520000 |
| 28 | 1989 | Guatemala | 59.600000 | 46.780000 | 0.680000 | 3.398988 | 4.919682 | 18.71 | 29.680 | 38.020000 | 55.030000 |
| 29 | 1989 | Honduras | 59.490000 | 48.180000 | 1.040000 | 1.841220 | 2.722716 | 16.90 | 29.140 | 38.600000 | 57.080000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 45.440000 | 35.480000 | 1.790000 | 0.379692 | 1.035198 | 1.73 | 5.570 | 6.360000 | 17.340000 |
| 79 | 2006 | Guatemala | 54.890000 | 43.560000 | 1.070000 | 1.552699 | 3.195781 | 3.93 | 9.270 | 11.510000 | 23.690000 |
| 80 | 2006 | Honduras | 57.420000 | 44.050000 | 0.580000 | 1.667679 | 2.614730 | 11.40 | 18.850 | 23.790000 | 37.300000 |
| 81 | 2007 | El Salvador | 45.240000 | 35.720000 | 1.930000 | 0.268951 | 0.835006 | 1.08 | 4.100 | 4.490000 | 13.940000 |
| 82 | 2007 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 83 | 2007 | Honduras | 56.160000 | 43.810000 | 0.900000 | 1.242759 | 2.279461 | 6.91 | 13.880 | 17.430000 | 31.970000 |
| 84 | 2008 | El Salvador | 46.650000 | 36.040000 | 1.700000 | 0.415200 | 1.114800 | 1.99 | 6.090 | 6.920000 | 18.580000 |
| 85 | 2008 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 86 | 2008 | Honduras | 55.740000 | 43.870000 | 0.910000 | 1.171764 | 2.130810 | 6.30 | 12.680 | 16.140000 | 29.350000 |
| 87 | 2009 | El Salvador | 45.930000 | 36.070000 | 1.780000 | 0.384678 | 1.054102 | 1.67 | 5.520 | 6.390000 | 17.510000 |
| 88 | 2009 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 89 | 2009 | Honduras | 51.560000 | 39.140000 | 1.150000 | 1.036152 | 1.979316 | 4.82 | 10.880 | 14.040000 | 26.820000 |
| 90 | 2010 | El Salvador | 44.530000 | 33.700000 | 1.670000 | 0.437296 | 1.120420 | 2.33 | 6.300 | 7.240000 | 18.550000 |
| 91 | 2010 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 92 | 2010 | Honduras | 53.390000 | 41.020000 | 1.090000 | 1.160250 | 2.183250 | 5.40 | 11.900 | 15.470000 | 29.110000 |
| 93 | 2011 | El Salvador | 42.430000 | 32.860000 | 2.110000 | 0.274518 | 0.911424 | 1.06 | 4.390 | 4.530000 | 15.040000 |
| 94 | 2011 | Guatemala | 52.350000 | 41.830000 | 1.340000 | 1.735265 | 3.983735 | 4.00 | 9.840 | 11.530000 | 26.470000 |
| 95 | 2011 | Honduras | 57.400000 | 45.670000 | 0.750000 | 1.428750 | 2.489454 | 7.88 | 14.660 | 18.750000 | 32.670000 |
| 96 | 2012 | El Salvador | 41.800000 | 32.470000 | 2.150000 | 0.252512 | 0.826127 | 0.98 | 3.840 | 4.160000 | 13.610000 |
| 97 | 2012 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 98 | 2012 | Honduras | 57.400000 | 45.680000 | 0.790000 | 1.653264 | 2.883924 | 9.25 | 17.100 | 21.360000 | 37.260000 |
| 99 | 2013 | El Salvador | 43.510000 | 34.350000 | 2.110000 | 0.197925 | 0.702177 | 0.74 | 3.160 | 3.250000 | 11.530000 |
| 100 | 2013 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 101 | 2013 | Honduras | 53.670000 | 41.480000 | 0.980000 | 1.486005 | 2.712175 | 7.66 | 15.240 | 18.930000 | 34.550000 |
| 102 | 2014 | El Salvador | 41.840000 | 32.310000 | 2.190000 | 0.181467 | 0.689819 | 0.64 | 3.000 | 2.970000 | 11.290000 |
| 103 | 2014 | Guatemala | 48.660000 | 38.360000 | 1.640000 | 1.493064 | 3.852810 | 2.72 | 8.110 | 9.320000 | 24.050000 |
| 104 | 2014 | Honduras | 50.640000 | 38.360000 | 1.150000 | 1.270416 | 2.484316 | 6.01 | 12.970 | 15.960000 | 31.210000 |
| 105 | 2015 | El Salvador | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 106 | 2015 | Guatemala | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 107 | 2015 | Honduras | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
108 rows × 11 columns
mig4
| Time | Time Code | Country Name | Country Code | Age dependency ratio (% of working-age population) [SP.POP.DPND] | Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] | Death rate, crude (per 1,000 people) [SP.DYN.CDRT.IN] | Fertility rate, total (births per woman) [SP.DYN.TFRT.IN] | Life expectancy at birth, total (years) [SP.DYN.LE00.IN] | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | YR1980 | El Salvador | SLV | 89.549266 | 37.353 | 11.681 | 5.087 | 56.529927 |
| 1 | 1980 | YR1980 | Guatemala | GTM | 93.787268 | 43.686 | 11.568 | 6.195 | 57.201488 |
| 2 | 1980 | YR1980 | Honduras | HND | 100.729300 | 43.476 | 10.233 | 6.313 | 59.612122 |
| 3 | 1981 | YR1981 | El Salvador | SLV | 88.861531 | 36.593 | 11.494 | 4.952 | 56.798976 |
| 4 | 1981 | YR1981 | Guatemala | GTM | 94.516037 | 43.384 | 11.300 | 6.161 | 57.632756 |
| 5 | 1981 | YR1981 | Honduras | HND | 100.456778 | 43.020 | 9.793 | 6.190 | 60.405854 |
| 6 | 1982 | YR1982 | El Salvador | SLV | 88.129782 | 35.833 | 11.251 | 4.819 | 57.197537 |
| 7 | 1982 | YR1982 | Guatemala | GTM | 95.163313 | 42.955 | 11.016 | 6.105 | 58.085951 |
| 8 | 1982 | YR1982 | Honduras | HND | 99.827463 | 42.524 | 9.359 | 6.062 | 61.212073 |
| 9 | 1983 | YR1983 | El Salvador | SLV | 87.385786 | 35.093 | 10.953 | 4.688 | 57.731659 |
| 10 | 1983 | YR1983 | Guatemala | GTM | 95.677113 | 42.406 | 10.718 | 6.027 | 58.558634 |
| 11 | 1983 | YR1983 | Honduras | HND | 99.020708 | 41.998 | 8.933 | 5.932 | 62.024805 |
| 12 | 1984 | YR1984 | El Salvador | SLV | 86.654026 | 34.383 | 10.602 | 4.562 | 58.399829 |
| 13 | 1984 | YR1984 | Guatemala | GTM | 95.981662 | 41.757 | 10.406 | 5.930 | 59.053829 |
| 14 | 1984 | YR1984 | Honduras | HND | 98.253395 | 41.452 | 8.520 | 5.802 | 62.831537 |
| 15 | 1985 | YR1985 | El Salvador | SLV | 85.923075 | 33.716 | 10.205 | 4.442 | 59.193976 |
| 16 | 1985 | YR1985 | Guatemala | GTM | 96.038252 | 41.053 | 10.085 | 5.820 | 59.568073 |
| 17 | 1985 | YR1985 | Honduras | HND | 97.609622 | 40.898 | 8.129 | 5.674 | 63.613756 |
| 18 | 1986 | YR1986 | El Salvador | SLV | 84.444327 | 33.096 | 9.775 | 4.328 | 60.099854 |
| 19 | 1986 | YR1986 | Guatemala | GTM | 96.304598 | 40.351 | 9.762 | 5.704 | 60.097317 |
| 20 | 1986 | YR1986 | Honduras | HND | 97.243943 | 40.353 | 7.769 | 5.553 | 64.350951 |
| 21 | 1987 | YR1987 | El Salvador | SLV | 83.083490 | 32.516 | 9.330 | 4.221 | 61.074000 |
| 22 | 1987 | YR1987 | Guatemala | GTM | 96.192946 | 39.705 | 9.444 | 5.591 | 60.633098 |
| 23 | 1987 | YR1987 | Honduras | HND | 96.907141 | 39.826 | 7.447 | 5.439 | 65.030146 |
| 24 | 1988 | YR1988 | El Salvador | SLV | 81.807159 | 31.963 | 8.893 | 4.118 | 62.071463 |
| 25 | 1988 | YR1988 | Guatemala | GTM | 95.809701 | 39.153 | 9.137 | 5.488 | 61.169878 |
| 26 | 1988 | YR1988 | Honduras | HND | 96.558949 | 39.319 | 7.164 | 5.333 | 65.645293 |
| 27 | 1989 | YR1989 | El Salvador | SLV | 80.578125 | 31.429 | 8.480 | 4.018 | 63.056415 |
| 28 | 1989 | YR1989 | Guatemala | GTM | 95.344674 | 38.707 | 8.845 | 5.396 | 61.704659 |
| 29 | 1989 | YR1989 | Honduras | HND | 96.118688 | 38.830 | 6.920 | 5.233 | 66.194439 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | YR2006 | El Salvador | SLV | 66.256872 | 19.887 | 6.672 | 2.322 | 70.479171 |
| 79 | 2006 | YR2006 | Guatemala | GTM | 82.841905 | 30.710 | 5.663 | 3.760 | 69.887902 |
| 80 | 2006 | YR2006 | Honduras | HND | 75.860438 | 26.318 | 5.105 | 3.164 | 71.672463 |
| 81 | 2007 | YR2007 | El Salvador | SLV | 64.944613 | 19.406 | 6.662 | 2.253 | 70.780463 |
| 82 | 2007 | YR2007 | Guatemala | GTM | 81.475709 | 30.085 | 5.616 | 3.664 | 70.110780 |
| 83 | 2007 | YR2007 | Honduras | HND | 73.754313 | 25.514 | 5.077 | 3.039 | 71.858732 |
| 84 | 2008 | YR2008 | El Salvador | SLV | 63.639250 | 18.969 | 6.659 | 2.189 | 71.080780 |
| 85 | 2008 | YR2008 | Guatemala | GTM | 80.024334 | 29.519 | 5.576 | 3.578 | 70.328146 |
| 86 | 2008 | YR2008 | Honduras | HND | 71.645904 | 24.728 | 5.055 | 2.918 | 72.039976 |
| 87 | 2009 | YR2009 | El Salvador | SLV | 62.302599 | 18.574 | 6.663 | 2.130 | 71.378146 |
| 88 | 2009 | YR2009 | Guatemala | GTM | 78.579556 | 29.016 | 5.539 | 3.501 | 70.547537 |
| 89 | 2009 | YR2009 | Honduras | HND | 69.528024 | 23.971 | 5.038 | 2.802 | 72.217220 |
| 90 | 2010 | YR2010 | El Salvador | SLV | 60.931929 | 18.223 | 6.673 | 2.078 | 71.670610 |
| 91 | 2010 | YR2010 | Guatemala | GTM | 77.198083 | 28.574 | 5.503 | 3.434 | 70.775463 |
| 92 | 2010 | YR2010 | Honduras | HND | 67.411337 | 23.261 | 5.026 | 2.695 | 72.393976 |
| 93 | 2011 | YR2011 | El Salvador | SLV | 59.472471 | 17.924 | 6.692 | 2.031 | 71.956171 |
| 94 | 2011 | YR2011 | Guatemala | GTM | 75.784683 | 28.182 | 5.467 | 3.373 | 71.010415 |
| 95 | 2011 | YR2011 | Honduras | HND | 65.342451 | 22.622 | 5.017 | 2.599 | 72.572732 |
| 96 | 2012 | YR2012 | El Salvador | SLV | 57.970629 | 17.676 | 6.718 | 1.991 | 72.231854 |
| 97 | 2012 | YR2012 | Guatemala | GTM | 74.496429 | 27.819 | 5.433 | 3.317 | 71.249390 |
| 98 | 2012 | YR2012 | Honduras | HND | 63.264970 | 22.065 | 5.012 | 2.514 | 72.755024 |
| 99 | 2013 | YR2013 | El Salvador | SLV | 56.531230 | 17.476 | 6.751 | 1.958 | 72.498146 |
| 100 | 2013 | YR2013 | Guatemala | GTM | 73.279326 | 27.465 | 5.401 | 3.263 | 71.486390 |
| 101 | 2013 | YR2013 | Honduras | HND | 61.250546 | 21.593 | 5.010 | 2.442 | 72.942854 |
| 102 | 2014 | YR2014 | El Salvador | SLV | 55.294848 | 17.314 | 6.790 | 1.931 | 72.754561 |
| 103 | 2014 | YR2014 | Guatemala | GTM | 72.069718 | 27.112 | 5.370 | 3.211 | 71.722415 |
| 104 | 2014 | YR2014 | Honduras | HND | 59.400971 | 21.203 | 5.011 | 2.382 | 73.135707 |
| 105 | 2015 | YR2015 | El Salvador | SLV | 54.321951 | 17.175 | 6.833 | 1.909 | 73.001098 |
| 106 | 2015 | YR2015 | Guatemala | GTM | 70.850672 | 26.752 | 5.339 | 3.159 | 71.956488 |
| 107 | 2015 | YR2015 | Honduras | HND | 57.768677 | 20.881 | 5.015 | 2.332 | 73.333122 |
108 rows × 9 columns
mig4.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 9 columns): Time 108 non-null int64 Time Code 108 non-null object Country Name 108 non-null object Country Code 108 non-null object Age dependency ratio (% of working-age population) [SP.POP.DPND] 108 non-null float64 Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] 108 non-null float64 Death rate, crude (per 1,000 people) [SP.DYN.CDRT.IN] 108 non-null float64 Fertility rate, total (births per woman) [SP.DYN.TFRT.IN] 108 non-null float64 Life expectancy at birth, total (years) [SP.DYN.LE00.IN] 108 non-null float64 dtypes: float64(5), int64(1), object(3) memory usage: 7.7+ KB
del mig4['Time Code']
del mig4['Country Code']
mig4.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 108 entries, 0 to 107 Data columns (total 7 columns): Time 108 non-null int64 Country Name 108 non-null object Age dependency ratio (% of working-age population) [SP.POP.DPND] 108 non-null float64 Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] 108 non-null float64 Death rate, crude (per 1,000 people) [SP.DYN.CDRT.IN] 108 non-null float64 Fertility rate, total (births per woman) [SP.DYN.TFRT.IN] 108 non-null float64 Life expectancy at birth, total (years) [SP.DYN.LE00.IN] 108 non-null float64 dtypes: float64(5), int64(1), object(1) memory usage: 6.0+ KB
mig4.rename(columns={
'Time': 'Year',
'Country Name': 'Country',
'Age dependency ratio (% of working-age population) [SP.POP.DPND]': 'age_dependency',
'Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN]': 'birth_rate',
'Death rate, crude (per 1,000 people) [SP.DYN.CDRT.IN]': 'death_rate',
'Fertility rate, total (births per woman) [SP.DYN.TFRT.IN]': 'fertility_rate',
'Life expectancy at birth, total (years) [SP.DYN.LE00.IN]': 'life_expectancy'
}, inplace=True)
mig4
| Year | Country | age_dependency | birth_rate | death_rate | fertility_rate | life_expectancy | |
|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 89.549266 | 37.353 | 11.681 | 5.087 | 56.529927 |
| 1 | 1980 | Guatemala | 93.787268 | 43.686 | 11.568 | 6.195 | 57.201488 |
| 2 | 1980 | Honduras | 100.729300 | 43.476 | 10.233 | 6.313 | 59.612122 |
| 3 | 1981 | El Salvador | 88.861531 | 36.593 | 11.494 | 4.952 | 56.798976 |
| 4 | 1981 | Guatemala | 94.516037 | 43.384 | 11.300 | 6.161 | 57.632756 |
| 5 | 1981 | Honduras | 100.456778 | 43.020 | 9.793 | 6.190 | 60.405854 |
| 6 | 1982 | El Salvador | 88.129782 | 35.833 | 11.251 | 4.819 | 57.197537 |
| 7 | 1982 | Guatemala | 95.163313 | 42.955 | 11.016 | 6.105 | 58.085951 |
| 8 | 1982 | Honduras | 99.827463 | 42.524 | 9.359 | 6.062 | 61.212073 |
| 9 | 1983 | El Salvador | 87.385786 | 35.093 | 10.953 | 4.688 | 57.731659 |
| 10 | 1983 | Guatemala | 95.677113 | 42.406 | 10.718 | 6.027 | 58.558634 |
| 11 | 1983 | Honduras | 99.020708 | 41.998 | 8.933 | 5.932 | 62.024805 |
| 12 | 1984 | El Salvador | 86.654026 | 34.383 | 10.602 | 4.562 | 58.399829 |
| 13 | 1984 | Guatemala | 95.981662 | 41.757 | 10.406 | 5.930 | 59.053829 |
| 14 | 1984 | Honduras | 98.253395 | 41.452 | 8.520 | 5.802 | 62.831537 |
| 15 | 1985 | El Salvador | 85.923075 | 33.716 | 10.205 | 4.442 | 59.193976 |
| 16 | 1985 | Guatemala | 96.038252 | 41.053 | 10.085 | 5.820 | 59.568073 |
| 17 | 1985 | Honduras | 97.609622 | 40.898 | 8.129 | 5.674 | 63.613756 |
| 18 | 1986 | El Salvador | 84.444327 | 33.096 | 9.775 | 4.328 | 60.099854 |
| 19 | 1986 | Guatemala | 96.304598 | 40.351 | 9.762 | 5.704 | 60.097317 |
| 20 | 1986 | Honduras | 97.243943 | 40.353 | 7.769 | 5.553 | 64.350951 |
| 21 | 1987 | El Salvador | 83.083490 | 32.516 | 9.330 | 4.221 | 61.074000 |
| 22 | 1987 | Guatemala | 96.192946 | 39.705 | 9.444 | 5.591 | 60.633098 |
| 23 | 1987 | Honduras | 96.907141 | 39.826 | 7.447 | 5.439 | 65.030146 |
| 24 | 1988 | El Salvador | 81.807159 | 31.963 | 8.893 | 4.118 | 62.071463 |
| 25 | 1988 | Guatemala | 95.809701 | 39.153 | 9.137 | 5.488 | 61.169878 |
| 26 | 1988 | Honduras | 96.558949 | 39.319 | 7.164 | 5.333 | 65.645293 |
| 27 | 1989 | El Salvador | 80.578125 | 31.429 | 8.480 | 4.018 | 63.056415 |
| 28 | 1989 | Guatemala | 95.344674 | 38.707 | 8.845 | 5.396 | 61.704659 |
| 29 | 1989 | Honduras | 96.118688 | 38.830 | 6.920 | 5.233 | 66.194439 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 66.256872 | 19.887 | 6.672 | 2.322 | 70.479171 |
| 79 | 2006 | Guatemala | 82.841905 | 30.710 | 5.663 | 3.760 | 69.887902 |
| 80 | 2006 | Honduras | 75.860438 | 26.318 | 5.105 | 3.164 | 71.672463 |
| 81 | 2007 | El Salvador | 64.944613 | 19.406 | 6.662 | 2.253 | 70.780463 |
| 82 | 2007 | Guatemala | 81.475709 | 30.085 | 5.616 | 3.664 | 70.110780 |
| 83 | 2007 | Honduras | 73.754313 | 25.514 | 5.077 | 3.039 | 71.858732 |
| 84 | 2008 | El Salvador | 63.639250 | 18.969 | 6.659 | 2.189 | 71.080780 |
| 85 | 2008 | Guatemala | 80.024334 | 29.519 | 5.576 | 3.578 | 70.328146 |
| 86 | 2008 | Honduras | 71.645904 | 24.728 | 5.055 | 2.918 | 72.039976 |
| 87 | 2009 | El Salvador | 62.302599 | 18.574 | 6.663 | 2.130 | 71.378146 |
| 88 | 2009 | Guatemala | 78.579556 | 29.016 | 5.539 | 3.501 | 70.547537 |
| 89 | 2009 | Honduras | 69.528024 | 23.971 | 5.038 | 2.802 | 72.217220 |
| 90 | 2010 | El Salvador | 60.931929 | 18.223 | 6.673 | 2.078 | 71.670610 |
| 91 | 2010 | Guatemala | 77.198083 | 28.574 | 5.503 | 3.434 | 70.775463 |
| 92 | 2010 | Honduras | 67.411337 | 23.261 | 5.026 | 2.695 | 72.393976 |
| 93 | 2011 | El Salvador | 59.472471 | 17.924 | 6.692 | 2.031 | 71.956171 |
| 94 | 2011 | Guatemala | 75.784683 | 28.182 | 5.467 | 3.373 | 71.010415 |
| 95 | 2011 | Honduras | 65.342451 | 22.622 | 5.017 | 2.599 | 72.572732 |
| 96 | 2012 | El Salvador | 57.970629 | 17.676 | 6.718 | 1.991 | 72.231854 |
| 97 | 2012 | Guatemala | 74.496429 | 27.819 | 5.433 | 3.317 | 71.249390 |
| 98 | 2012 | Honduras | 63.264970 | 22.065 | 5.012 | 2.514 | 72.755024 |
| 99 | 2013 | El Salvador | 56.531230 | 17.476 | 6.751 | 1.958 | 72.498146 |
| 100 | 2013 | Guatemala | 73.279326 | 27.465 | 5.401 | 3.263 | 71.486390 |
| 101 | 2013 | Honduras | 61.250546 | 21.593 | 5.010 | 2.442 | 72.942854 |
| 102 | 2014 | El Salvador | 55.294848 | 17.314 | 6.790 | 1.931 | 72.754561 |
| 103 | 2014 | Guatemala | 72.069718 | 27.112 | 5.370 | 3.211 | 71.722415 |
| 104 | 2014 | Honduras | 59.400971 | 21.203 | 5.011 | 2.382 | 73.135707 |
| 105 | 2015 | El Salvador | 54.321951 | 17.175 | 6.833 | 1.909 | 73.001098 |
| 106 | 2015 | Guatemala | 70.850672 | 26.752 | 5.339 | 3.159 | 71.956488 |
| 107 | 2015 | Honduras | 57.768677 | 20.881 | 5.015 | 2.332 | 73.333122 |
108 rows × 7 columns
mig5.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 116 entries, 0 to 115 Data columns (total 18 columns): Time 113 non-null object Time Code 111 non-null object Country Name 111 non-null object Country Code 111 non-null object Unemployment, total (% of total labor force) (national estimate) [SL.UEM.TOTL.NE.ZS] 111 non-null object Trade (% of GDP) [NE.TRD.GNFS.ZS] 111 non-null object Short-term debt (% of total external debt) [DT.DOD.DSTC.ZS] 111 non-null object Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [SI.POV.DDAY] 111 non-null object Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population) [SI.POV.2DAY] 111 non-null object Population growth (annual %) [SP.POP.GROW] 111 non-null object Personal remittances, received (% of GDP) [BX.TRF.PWKR.DT.GD.ZS] 111 non-null object Net bilateral aid flows from DAC donors, United States (current US$) [DC.DAC.USAL.CD] 111 non-null object Imports of goods and services (% of GDP) [NE.IMP.GNFS.ZS] 111 non-null object General government final consumption expenditure (% of GDP) [NE.CON.GOVT.ZS] 111 non-null object Foreign direct investment, net outflows (% of GDP) [BM.KLT.DINV.WD.GD.ZS] 111 non-null object Final consumption expenditure, etc. (% of GDP) [NE.CON.TETC.ZS] 111 non-null object Exports of goods and services (% of GDP) [NE.EXP.GNFS.ZS] 111 non-null object Employment to population ratio, 15+, total (%) (national estimate) [SL.EMP.TOTL.SP.NE.ZS] 111 non-null object dtypes: object(18) memory usage: 16.4+ KB
del mig5['Time Code']
del mig5['Country Code']
del mig5['Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [SI.POV.DDAY]']
del mig5['Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population) [SI.POV.2DAY]']
del mig5['Final consumption expenditure, etc. (% of GDP) [NE.CON.TETC.ZS]']
mig5.rename(columns={
'Time': 'Year',
'Country Name': 'Country',
'Unemployment, total (% of total labor force) (national estimate) [SL.UEM.TOTL.NE.ZS]': 'unemployment',
'Trade (% of GDP) [NE.TRD.GNFS.ZS]': 'trade',
'Short-term debt (% of total external debt) [DT.DOD.DSTC.ZS]': 'short_term_debt',
'Population growth (annual %) [SP.POP.GROW]': 'pop_growth',
'Personal remittances, received (% of GDP) [BX.TRF.PWKR.DT.GD.ZS]': 'remittances',
'Net bilateral aid flows from DAC donors, United States (current US$) [DC.DAC.USAL.CD]': 'net_bilateral_aid',
'Imports of goods and services (% of GDP) [NE.IMP.GNFS.ZS]': 'imports_%GDP',
'General government final consumption expenditure (% of GDP) [NE.CON.GOVT.ZS]': 'gov_consumption',
'Foreign direct investment, net outflows (% of GDP) [BM.KLT.DINV.WD.GD.ZS]': 'FDI',
'Exports of goods and services (% of GDP) [NE.EXP.GNFS.ZS]': 'exports_%GDP',
'Employment to population ratio, 15+, total (%) (national estimate) [SL.EMP.TOTL.SP.NE.ZS]': 'employment_15+'
}, inplace=True)
mig5
| Year | Country | unemployment | trade | short_term_debt | pop_growth | remittances | net_bilateral_aid | imports_%GDP | gov_consumption | FDI | exports_%GDP | employment_15+ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 13.34000015 | 67.40646419 | 22.07 | 1.739183852 | 1.372147486 | 43000000 | 33.24491693 | 13.98896501 | .. | 34.16154725 | .. |
| 1 | 1980 | Guatemala | .. | 47.10548695 | 26.383 | 2.635142783 | 0.33254218 | 17000000 | 24.91908564 | 7.958165687 | 0.025384899 | 22.18640131 | 49.61000061 |
| 2 | 1980 | Honduras | .. | 81.29383902 | 17.3081 | 3.14529964 | 0.062353858 | 19000000 | 44.05689509 | 12.66562719 | 0.038971161 | 37.23694394 | 27.37999916 |
| 3 | 1981 | El Salvador | .. | 60.26649248 | 17.3809 | 1.611672741 | 2.108693097 | 97000000 | 33.58780207 | 15.82916235 | .. | 26.67869041 | .. |
| 4 | 1981 | Guatemala | 2.150000095 | 40.69125737 | 9.9649 | 2.658257123 | 0.284635482 | 18000000 | 23.60150949 | 7.900086858 | -0.011617775 | 17.08974788 | .. |
| 5 | 1981 | Honduras | .. | 69.3385352 | 13.3695 | 3.113439292 | 0.062067743 | 35000000 | 37.70172016 | 12.78595496 | 0.070934563 | 31.63681504 | 26.43000031 |
| 6 | 1982 | El Salvador | .. | 51.24774023 | 13.4222 | 1.498033738 | 3.300787147 | 170000000 | 28.47033723 | 15.7782337 | .. | 22.777403 | .. |
| 7 | 1982 | Guatemala | 2.269999981 | 33.47481818 | 7.235 | 2.669298084 | 0.122748654 | 20000000 | 18.68762138 | 7.743489999 | -0.045887348 | 14.78719679 | .. |
| 8 | 1982 | Honduras | 7.300000191 | 54.72705089 | 7.7859 | 3.083348528 | 0.051661787 | 68000000 | 28.05234841 | 13.05321142 | -0.034441191 | 26.67470249 | 40.09000015 |
| 9 | 1983 | El Salvador | .. | 54.39780104 | 5.1284 | 1.413013599 | 3.292314653 | 231000000 | 29.90928339 | 15.82944795 | .. | 24.48851765 | .. |
| 10 | 1983 | Guatemala | .. | 27.546959 | 6.1899 | 2.654604276 | 0.043093922 | 36000000 | 14.55248444 | 7.602209609 | 0 | 12.99447456 | .. |
| 11 | 1983 | Honduras | .. | 55.39486765 | 6.3325 | 3.056877686 | 0.058498537 | 64000000 | 29.23302127 | 13.11342238 | 0.064998376 | 26.16184638 | 35.59999847 |
| 12 | 1984 | El Salvador | .. | 50.29067505 | 5.8131 | 1.364659688 | 4.345542138 | 221000000 | 28.53653596 | 16.036198 | .. | 21.75413909 | .. |
| 13 | 1984 | Guatemala | .. | 28.1531148 | 6.1022 | 2.607323499 | 0.035902852 | 29000000 | 15.15100301 | 7.665258631 | 0.05279831 | 13.0021118 | .. |
| 14 | 1984 | Honduras | .. | 57.72823139 | 8.26 | 3.037318621 | 0.058752638 | 123000000 | 32.02771919 | 13.19674601 | -0.030129557 | 25.7005122 | 38.72000122 |
| 15 | 1985 | El Salvador | 16.95000076 | 52.21053821 | 4.3227 | 1.343117088 | 4.135388438 | 287000000 | 29.88677204 | 15.4905211 | .. | 22.32376617 | .. |
| 16 | 1985 | Guatemala | .. | 24.93224559 | 9.564 | 2.541226177 | 0.010286318 | 50000000 | 12.98401607 | 6.953550631 | 0 | 11.94822952 | 51.29999924 |
| 17 | 1985 | Honduras | .. | 54.96634366 | 10.6047 | 3.020230628 | 0.057700232 | 161000000 | 29.86674172 | 13.09245794 | 0 | 25.09960194 | .. |
| 18 | 1986 | El Salvador | 7.900000095 | 53.71412272 | 6.5223 | 1.324193255 | 4.170337688 | 272000000 | 29.04596041 | 14.18109655 | .. | 24.66816231 | .. |
| 19 | 1986 | Guatemala | .. | 30.64401925 | 10.711 | 2.470000701 | 0.009679252 | 86000000 | 14.59211949 | 7.096224226 | 0 | 16.05189976 | .. |
| 20 | 1986 | Honduras | 12.11999989 | 54.89037607 | 11.6717 | 2.998073065 | 0.055139816 | 175000000 | 28.30510663 | 14.27071138 | 0 | 26.58526944 | .. |
| 21 | 1987 | El Salvador | .. | 45.0946222 | 10.3707 | 1.302156496 | 4.715458305 | 356000000 | 26.10231797 | 13.74570501 | .. | 18.99230423 | .. |
| 22 | 1987 | Guatemala | 3.5 | 38.14296343 | 9.8328 | 2.413838221 | 0.001411552 | 155000000 | 22.29405505 | 7.902433694 | 0.014115522 | 15.84890838 | .. |
| 23 | 1987 | Honduras | 11.39999962 | 48.7898862 | 11.9562 | 2.966596303 | 0.81155933 | 153000000 | 25.82781488 | 14.22034815 | 0.024081879 | 22.96207132 | .. |
| 24 | 1988 | El Salvador | 9.369999886 | 38.09570441 | 11.637 | 1.291964388 | 5.029738453 | 318000000 | 22.28502248 | 12.73167251 | .. | 15.81068193 | .. |
| 25 | 1988 | Guatemala | .. | 38.03991399 | 11.0787 | 2.383683528 | 0.582789026 | 134000000 | 21.93623865 | 7.981504735 | -0.012752495 | 16.10367533 | .. |
| 26 | 1988 | Honduras | .. | 55.21565236 | 12.1001 | 2.927378347 | 1.052794273 | 155000000 | 28.92660253 | 14.139012 | -0.025186466 | 26.28904983 | .. |
| 27 | 1989 | El Salvador | 8.350000381 | 36.92829582 | 9.7005 | 1.293850446 | 5.439348042 | 310000000 | 23.69050765 | 12.19413417 | .. | 13.23778818 | .. |
| 28 | 1989 | Guatemala | 2 | 39.78154615 | 12.1191 | 2.387014157 | 1.010614501 | 146000000 | 22.47409728 | 7.895425786 | -0.047558329 | 17.30744887 | .. |
| 29 | 1989 | Honduras | .. | 65.34739631 | 11.4579 | 2.879679331 | 1.363847438 | 102000000 | 34.34294528 | 14.27327352 | -0.028062705 | 31.00445103 | .. |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 86 | 2008 | Honduras | 2.99000001 | 135.7489552 | 13.3425 | 1.747160287 | 20.45975298 | 96330000 | 84.42367851 | 17.11482759 | 1.402888726 | 51.32527669 | .. |
| 87 | 2009 | El Salvador | 7.329999924 | 61.87164222 | 8.3258 | 0.285541824 | 16.46745075 | 82080000 | 38.67479793 | 10.62920478 | 0.014326509 | 23.1968443 | 58.15999985 |
| 88 | 2009 | Guatemala | .. | 57.1059931 | 8.2714 | 2.183077451 | 10.65185852 | 83890000 | 33.13058888 | 10.17902302 | 0.325370676 | 23.97540421 | .. |
| 89 | 2009 | Honduras | 3.279999971 | 96.90500602 | 6.5494 | 1.688651031 | 17.10144713 | 128760000 | 57.3747552 | 18.69973827 | -0.074316898 | 39.53025082 | 59.31999969 |
| 90 | 2010 | El Salvador | 7.050000191 | 68.76876316 | 7.5574 | 0.2809033 | 16.20967453 | 148160000 | 42.84420332 | 10.70486453 | 0.524806817 | 25.92455984 | 58.11000061 |
| 91 | 2010 | Guatemala | 3.74000001 | 62.11493226 | 10.5394 | 2.156000018 | 10.23686713 | 100500000 | 36.30918223 | 10.47586426 | 0.153036418 | 25.80575003 | 42.91999817 |
| 92 | 2010 | Honduras | 4.099999905 | 109.4418382 | 9.411 | 1.622622006 | 16.64314011 | 100840000 | 63.68293204 | 17.92643157 | -2.309011866 | 45.7589062 | 59.36000061 |
| 93 | 2011 | El Salvador | 6.619999886 | 74.64324301 | 10.1925 | 0.279521916 | 15.74808439 | 162440000 | 46.6632093 | 11.05579325 | -0.414577558 | 27.98003371 | 58.54999924 |
| 94 | 2011 | Guatemala | 4.130000114 | 63.98419583 | 13.9156 | 2.129043456 | 9.492784648 | 93080000 | 37.3587707 | 10.18922762 | 0.274375676 | 26.62542512 | 59.22999954 |
| 95 | 2011 | Honduras | 4.269999981 | 122.2169026 | 6.0942 | 1.554236301 | 15.98008307 | 46360000 | 70.95921592 | 16.06436839 | 0.172123867 | 51.25768668 | 49.65999985 |
| 96 | 2012 | El Salvador | 6.070000172 | 69.69882756 | 11.7092 | 0.280768403 | 16.36100124 | 150850000 | 44.0764941 | 11.22845769 | -0.150728911 | 25.62233346 | 59.40000153 |
| 97 | 2012 | Guatemala | 2.869999886 | 60.98247455 | 4.5907 | 2.100666307 | 9.983741683 | 95490000 | 36.11378065 | 10.34773147 | 0.115397798 | 24.8686939 | 63.54000092 |
| 98 | 2012 | Honduras | .. | 121.1882158 | 7.9444 | 1.493977504 | 15.87111001 | 52650000 | 70.28541699 | 16.20178304 | 1.177095279 | 50.90279882 | .. |
| 99 | 2013 | El Salvador | 5.929999828 | 71.94888074 | 13.6861 | 0.286321131 | 16.24140415 | 51090000 | 45.57737086 | 11.5642543 | 0.271335347 | 26.37150988 | 59.88000107 |
| 100 | 2013 | Guatemala | 2.99000001 | 58.54834136 | 4.136 | 2.073729463 | 9.988782489 | 102670000 | 34.82864877 | 10.57001277 | 0.169948314 | 23.71969259 | 58.34000015 |
| 101 | 2013 | Honduras | 3.910000086 | 116.3060492 | 7.4932 | 1.449196017 | 16.86376042 | 90910000 | 68.36455893 | 16.73381325 | 0.421417344 | 47.94149032 | 51.58000183 |
| 102 | 2014 | El Salvador | .. | 69.57077057 | 13.8859 | 0.296162908 | 16.56788621 | 45370000 | 43.70005827 | 11.52261896 | 0.791025609 | 25.8707123 | .. |
| 103 | 2014 | Guatemala | 2.910000086 | 56.71791511 | 3.9421 | 2.048252145 | 9.941700892 | 126040000 | 33.55949655 | 10.84853306 | -0.198735021 | 23.15841855 | 58.40999985 |
| 104 | 2014 | Honduras | .. | 112.6092346 | 6.8729 | 1.424638087 | 17.38569494 | 80450000 | 65.73989464 | 15.71007371 | 0.898222966 | 46.86933991 | .. |
| 105 | 2015 | El Salvador | .. | 67.9890291 | 13.4668 | 0.308591942 | 16.57714726 | 47470000 | 42.03023574 | 11.89313816 | 0.346950619 | 25.95879336 | .. |
| 106 | 2015 | Guatemala | 2.420000076 | 51.33340339 | 3.6161 | 2.023674003 | 10.30311519 | 123500000 | 30.04344079 | 10.36561731 | 0.049518645 | 21.2899626 | 58.90000153 |
| 107 | 2015 | Honduras | 7.380000114 | 107.4349161 | 6.4723 | 1.414026656 | 17.95312289 | 110380000 | 62.58871456 | 14.70432469 | 0.998199897 | 44.84620151 | 60.63000107 |
| 108 | 2016 | El Salvador | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 109 | 2016 | Guatemala | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 110 | 2016 | Honduras | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 111 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 112 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 113 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 114 | Data from database: World Development Indicators | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 115 | Last Updated: 04/27/2017 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
116 rows × 13 columns
mig6 = mig5.drop([108,109,110,111,112,113,114,115])
mig6['unemployment'] = pd.to_numeric(mig6['unemployment'], errors='coerce')
mig6['trade'] = pd.to_numeric(mig6['trade'], errors='coerce')
mig6['short_term_debt'] = pd.to_numeric(mig6['short_term_debt'], errors='coerce')
mig6['pop_growth'] = pd.to_numeric(mig6['pop_growth'], errors='coerce')
mig6['remittances'] = pd.to_numeric(mig6['remittances'], errors='coerce')
mig6['net_bilateral_aid'] = pd.to_numeric(mig6['net_bilateral_aid'], errors='coerce')
mig6['imports_%GDP'] = pd.to_numeric(mig6['imports_%GDP'], errors='coerce')
mig6['gov_consumption'] = pd.to_numeric(mig6['gov_consumption'], errors='coerce')
mig6['FDI'] = pd.to_numeric(mig6['FDI'], errors='coerce')
mig6['exports_%GDP'] = pd.to_numeric(mig6['exports_%GDP'], errors='coerce')
mig6['employment_15+'] = pd.to_numeric(mig6['employment_15+'], errors='coerce')
mig6['unemployment'].fillna(np.mean(mig6['unemployment']), inplace=True)
mig6['FDI'].fillna(np.mean(mig6['FDI']), inplace=True)
mig6['employment_15+'].fillna(np.mean(mig6['employment_15+']), inplace=True)
mig6
| Year | Country | unemployment | trade | short_term_debt | pop_growth | remittances | net_bilateral_aid | imports_%GDP | gov_consumption | FDI | exports_%GDP | employment_15+ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 13.340000 | 67.406464 | 22.0700 | 1.739184 | 1.372147 | 43000000 | 33.244917 | 13.988965 | 0.014116 | 34.161547 | 51.230213 |
| 1 | 1980 | Guatemala | 5.496944 | 47.105487 | 26.3830 | 2.635143 | 0.332542 | 17000000 | 24.919086 | 7.958166 | 0.025385 | 22.186401 | 49.610001 |
| 2 | 1980 | Honduras | 5.496944 | 81.293839 | 17.3081 | 3.145300 | 0.062354 | 19000000 | 44.056895 | 12.665627 | 0.038971 | 37.236944 | 27.379999 |
| 3 | 1981 | El Salvador | 5.496944 | 60.266492 | 17.3809 | 1.611673 | 2.108693 | 97000000 | 33.587802 | 15.829162 | 0.014116 | 26.678690 | 51.230213 |
| 4 | 1981 | Guatemala | 2.150000 | 40.691257 | 9.9649 | 2.658257 | 0.284635 | 18000000 | 23.601509 | 7.900087 | -0.011618 | 17.089748 | 51.230213 |
| 5 | 1981 | Honduras | 5.496944 | 69.338535 | 13.3695 | 3.113439 | 0.062068 | 35000000 | 37.701720 | 12.785955 | 0.070935 | 31.636815 | 26.430000 |
| 6 | 1982 | El Salvador | 5.496944 | 51.247740 | 13.4222 | 1.498034 | 3.300787 | 170000000 | 28.470337 | 15.778234 | 0.014116 | 22.777403 | 51.230213 |
| 7 | 1982 | Guatemala | 2.270000 | 33.474818 | 7.2350 | 2.669298 | 0.122749 | 20000000 | 18.687621 | 7.743490 | -0.045887 | 14.787197 | 51.230213 |
| 8 | 1982 | Honduras | 7.300000 | 54.727051 | 7.7859 | 3.083349 | 0.051662 | 68000000 | 28.052348 | 13.053211 | -0.034441 | 26.674702 | 40.090000 |
| 9 | 1983 | El Salvador | 5.496944 | 54.397801 | 5.1284 | 1.413014 | 3.292315 | 231000000 | 29.909283 | 15.829448 | 0.014116 | 24.488518 | 51.230213 |
| 10 | 1983 | Guatemala | 5.496944 | 27.546959 | 6.1899 | 2.654604 | 0.043094 | 36000000 | 14.552484 | 7.602210 | 0.000000 | 12.994475 | 51.230213 |
| 11 | 1983 | Honduras | 5.496944 | 55.394868 | 6.3325 | 3.056878 | 0.058499 | 64000000 | 29.233021 | 13.113422 | 0.064998 | 26.161846 | 35.599998 |
| 12 | 1984 | El Salvador | 5.496944 | 50.290675 | 5.8131 | 1.364660 | 4.345542 | 221000000 | 28.536536 | 16.036198 | 0.014116 | 21.754139 | 51.230213 |
| 13 | 1984 | Guatemala | 5.496944 | 28.153115 | 6.1022 | 2.607323 | 0.035903 | 29000000 | 15.151003 | 7.665259 | 0.052798 | 13.002112 | 51.230213 |
| 14 | 1984 | Honduras | 5.496944 | 57.728231 | 8.2600 | 3.037319 | 0.058753 | 123000000 | 32.027719 | 13.196746 | -0.030130 | 25.700512 | 38.720001 |
| 15 | 1985 | El Salvador | 16.950001 | 52.210538 | 4.3227 | 1.343117 | 4.135388 | 287000000 | 29.886772 | 15.490521 | 0.014116 | 22.323766 | 51.230213 |
| 16 | 1985 | Guatemala | 5.496944 | 24.932246 | 9.5640 | 2.541226 | 0.010286 | 50000000 | 12.984016 | 6.953551 | 0.000000 | 11.948230 | 51.299999 |
| 17 | 1985 | Honduras | 5.496944 | 54.966344 | 10.6047 | 3.020231 | 0.057700 | 161000000 | 29.866742 | 13.092458 | 0.000000 | 25.099602 | 51.230213 |
| 18 | 1986 | El Salvador | 7.900000 | 53.714123 | 6.5223 | 1.324193 | 4.170338 | 272000000 | 29.045960 | 14.181097 | 0.014116 | 24.668162 | 51.230213 |
| 19 | 1986 | Guatemala | 5.496944 | 30.644019 | 10.7110 | 2.470001 | 0.009679 | 86000000 | 14.592119 | 7.096224 | 0.000000 | 16.051900 | 51.230213 |
| 20 | 1986 | Honduras | 12.120000 | 54.890376 | 11.6717 | 2.998073 | 0.055140 | 175000000 | 28.305107 | 14.270711 | 0.000000 | 26.585269 | 51.230213 |
| 21 | 1987 | El Salvador | 5.496944 | 45.094622 | 10.3707 | 1.302156 | 4.715458 | 356000000 | 26.102318 | 13.745705 | 0.014116 | 18.992304 | 51.230213 |
| 22 | 1987 | Guatemala | 3.500000 | 38.142963 | 9.8328 | 2.413838 | 0.001412 | 155000000 | 22.294055 | 7.902434 | 0.014116 | 15.848908 | 51.230213 |
| 23 | 1987 | Honduras | 11.400000 | 48.789886 | 11.9562 | 2.966596 | 0.811559 | 153000000 | 25.827815 | 14.220348 | 0.024082 | 22.962071 | 51.230213 |
| 24 | 1988 | El Salvador | 9.370000 | 38.095704 | 11.6370 | 1.291964 | 5.029738 | 318000000 | 22.285022 | 12.731673 | 0.014116 | 15.810682 | 51.230213 |
| 25 | 1988 | Guatemala | 5.496944 | 38.039914 | 11.0787 | 2.383684 | 0.582789 | 134000000 | 21.936239 | 7.981505 | -0.012752 | 16.103675 | 51.230213 |
| 26 | 1988 | Honduras | 5.496944 | 55.215652 | 12.1001 | 2.927378 | 1.052794 | 155000000 | 28.926603 | 14.139012 | -0.025186 | 26.289050 | 51.230213 |
| 27 | 1989 | El Salvador | 8.350000 | 36.928296 | 9.7005 | 1.293850 | 5.439348 | 310000000 | 23.690508 | 12.194134 | 0.014116 | 13.237788 | 51.230213 |
| 28 | 1989 | Guatemala | 2.000000 | 39.781546 | 12.1191 | 2.387014 | 1.010615 | 146000000 | 22.474097 | 7.895426 | -0.047558 | 17.307449 | 51.230213 |
| 29 | 1989 | Honduras | 5.496944 | 65.347396 | 11.4579 | 2.879679 | 1.363847 | 102000000 | 34.342945 | 14.273274 | -0.028063 | 31.004451 | 51.230213 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 6.570000 | 71.849041 | 12.6699 | 0.341593 | 18.773955 | 24540000 | 46.166991 | 9.826583 | -0.141774 | 25.682050 | 49.169998 |
| 79 | 2006 | Guatemala | 1.820000 | 66.818187 | 13.4658 | 2.298528 | 12.239366 | 67250000 | 41.886457 | 8.369963 | 0.276205 | 24.931730 | 57.549999 |
| 80 | 2006 | Honduras | 3.110000 | 133.131835 | 7.1408 | 1.826883 | 21.557383 | 84100000 | 77.077193 | 15.003781 | 0.452738 | 56.054642 | 49.080002 |
| 81 | 2007 | El Salvador | 6.410000 | 74.177439 | 13.1110 | 0.315511 | 18.448488 | 39040000 | 48.294694 | 9.281817 | 0.473516 | 25.882745 | 49.500000 |
| 82 | 2007 | Guatemala | 5.496944 | 67.898497 | 15.8088 | 2.254506 | 12.418103 | 45710000 | 42.333233 | 8.658056 | 0.408934 | 25.565264 | 51.230213 |
| 83 | 2007 | Honduras | 2.920000 | 135.070635 | 10.5623 | 1.792143 | 21.291564 | 71100000 | 81.561623 | 16.602374 | 0.332742 | 53.509012 | 51.230213 |
| 84 | 2008 | El Salvador | 5.880000 | 76.580188 | 14.4812 | 0.296649 | 17.520181 | 42370000 | 49.698567 | 9.175027 | 0.370631 | 26.881620 | 59.020000 |
| 85 | 2008 | Guatemala | 5.496944 | 64.125228 | 15.5632 | 2.215217 | 11.395396 | 70350000 | 39.406974 | 9.013268 | 0.034880 | 24.718254 | 51.230213 |
| 86 | 2008 | Honduras | 2.990000 | 135.748955 | 13.3425 | 1.747160 | 20.459753 | 96330000 | 84.423679 | 17.114828 | 1.402889 | 51.325277 | 51.230213 |
| 87 | 2009 | El Salvador | 7.330000 | 61.871642 | 8.3258 | 0.285542 | 16.467451 | 82080000 | 38.674798 | 10.629205 | 0.014327 | 23.196844 | 58.160000 |
| 88 | 2009 | Guatemala | 5.496944 | 57.105993 | 8.2714 | 2.183077 | 10.651859 | 83890000 | 33.130589 | 10.179023 | 0.325371 | 23.975404 | 51.230213 |
| 89 | 2009 | Honduras | 3.280000 | 96.905006 | 6.5494 | 1.688651 | 17.101447 | 128760000 | 57.374755 | 18.699738 | -0.074317 | 39.530251 | 59.320000 |
| 90 | 2010 | El Salvador | 7.050000 | 68.768763 | 7.5574 | 0.280903 | 16.209675 | 148160000 | 42.844203 | 10.704865 | 0.524807 | 25.924560 | 58.110001 |
| 91 | 2010 | Guatemala | 3.740000 | 62.114932 | 10.5394 | 2.156000 | 10.236867 | 100500000 | 36.309182 | 10.475864 | 0.153036 | 25.805750 | 42.919998 |
| 92 | 2010 | Honduras | 4.100000 | 109.441838 | 9.4110 | 1.622622 | 16.643140 | 100840000 | 63.682932 | 17.926432 | -2.309012 | 45.758906 | 59.360001 |
| 93 | 2011 | El Salvador | 6.620000 | 74.643243 | 10.1925 | 0.279522 | 15.748084 | 162440000 | 46.663209 | 11.055793 | -0.414578 | 27.980034 | 58.549999 |
| 94 | 2011 | Guatemala | 4.130000 | 63.984196 | 13.9156 | 2.129043 | 9.492785 | 93080000 | 37.358771 | 10.189228 | 0.274376 | 26.625425 | 59.230000 |
| 95 | 2011 | Honduras | 4.270000 | 122.216903 | 6.0942 | 1.554236 | 15.980083 | 46360000 | 70.959216 | 16.064368 | 0.172124 | 51.257687 | 49.660000 |
| 96 | 2012 | El Salvador | 6.070000 | 69.698828 | 11.7092 | 0.280768 | 16.361001 | 150850000 | 44.076494 | 11.228458 | -0.150729 | 25.622333 | 59.400002 |
| 97 | 2012 | Guatemala | 2.870000 | 60.982475 | 4.5907 | 2.100666 | 9.983742 | 95490000 | 36.113781 | 10.347731 | 0.115398 | 24.868694 | 63.540001 |
| 98 | 2012 | Honduras | 5.496944 | 121.188216 | 7.9444 | 1.493978 | 15.871110 | 52650000 | 70.285417 | 16.201783 | 1.177095 | 50.902799 | 51.230213 |
| 99 | 2013 | El Salvador | 5.930000 | 71.948881 | 13.6861 | 0.286321 | 16.241404 | 51090000 | 45.577371 | 11.564254 | 0.271335 | 26.371510 | 59.880001 |
| 100 | 2013 | Guatemala | 2.990000 | 58.548341 | 4.1360 | 2.073729 | 9.988782 | 102670000 | 34.828649 | 10.570013 | 0.169948 | 23.719693 | 58.340000 |
| 101 | 2013 | Honduras | 3.910000 | 116.306049 | 7.4932 | 1.449196 | 16.863760 | 90910000 | 68.364559 | 16.733813 | 0.421417 | 47.941490 | 51.580002 |
| 102 | 2014 | El Salvador | 5.496944 | 69.570771 | 13.8859 | 0.296163 | 16.567886 | 45370000 | 43.700058 | 11.522619 | 0.791026 | 25.870712 | 51.230213 |
| 103 | 2014 | Guatemala | 2.910000 | 56.717915 | 3.9421 | 2.048252 | 9.941701 | 126040000 | 33.559497 | 10.848533 | -0.198735 | 23.158419 | 58.410000 |
| 104 | 2014 | Honduras | 5.496944 | 112.609235 | 6.8729 | 1.424638 | 17.385695 | 80450000 | 65.739895 | 15.710074 | 0.898223 | 46.869340 | 51.230213 |
| 105 | 2015 | El Salvador | 5.496944 | 67.989029 | 13.4668 | 0.308592 | 16.577147 | 47470000 | 42.030236 | 11.893138 | 0.346951 | 25.958793 | 51.230213 |
| 106 | 2015 | Guatemala | 2.420000 | 51.333403 | 3.6161 | 2.023674 | 10.303115 | 123500000 | 30.043441 | 10.365617 | 0.049519 | 21.289963 | 58.900002 |
| 107 | 2015 | Honduras | 7.380000 | 107.434916 | 6.4723 | 1.414027 | 17.953123 | 110380000 | 62.588715 | 14.704325 | 0.998200 | 44.846202 | 60.630001 |
108 rows × 13 columns
mig6.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 108 entries, 0 to 107 Data columns (total 13 columns): Year 108 non-null object Country 108 non-null object unemployment 108 non-null float64 trade 108 non-null float64 short_term_debt 108 non-null float64 pop_growth 108 non-null float64 remittances 108 non-null float64 net_bilateral_aid 108 non-null int64 imports_%GDP 108 non-null float64 gov_consumption 108 non-null float64 FDI 108 non-null float64 exports_%GDP 108 non-null float64 employment_15+ 108 non-null float64 dtypes: float64(10), int64(1), object(2) memory usage: 11.8+ KB
mig6.Year = [int(float(x)) for x in mig6.Year]
mig6.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 108 entries, 0 to 107 Data columns (total 13 columns): Year 108 non-null int64 Country 108 non-null object unemployment 108 non-null float64 trade 108 non-null float64 short_term_debt 108 non-null float64 pop_growth 108 non-null float64 remittances 108 non-null float64 net_bilateral_aid 108 non-null int64 imports_%GDP 108 non-null float64 gov_consumption 108 non-null float64 FDI 108 non-null float64 exports_%GDP 108 non-null float64 employment_15+ 108 non-null float64 dtypes: float64(10), int64(2), object(1) memory usage: 11.8+ KB
migm1 = pd.merge(mig1, mig2, on=['Year', 'Country'], how='right')
migm1
| Year | Country | Migration | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 | 1516.400016 | 2572.813235 | 43.742478 | 52.756733 | 73.720787 |
| 1 | 1980 | Guatemala | 0.886027 | 1516.400016 | 2560.782037 | 45.444923 | 51.602977 | 33.904148 |
| 2 | 1980 | Honduras | 1.076884 | 713.525940 | 1655.946421 | 46.957200 | 49.818337 | 44.575001 |
| 3 | 1981 | El Salvador | 1.342039 | 1516.400016 | 2267.095959 | 43.481122 | 52.948845 | 46.450790 |
| 4 | 1981 | Guatemala | 1.342039 | 493.277863 | 2509.736778 | 45.617358 | 51.409643 | 33.957420 |
| 5 | 1981 | Honduras | 1.342039 | 821.092712 | 1645.846419 | 46.892259 | 49.886066 | 73.720787 |
| 6 | 1982 | El Salvador | 1.342039 | 999.595276 | 2092.554425 | 43.204606 | 53.154795 | 49.382709 |
| 7 | 1982 | Guatemala | 1.342039 | 1516.400016 | 2357.368296 | 45.771834 | 51.239138 | 33.880711 |
| 8 | 1982 | Honduras | 1.342039 | 864.464600 | 1573.671559 | 46.745647 | 50.043171 | 49.944939 |
| 9 | 1983 | El Salvador | 1.342039 | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.613579 |
| 10 | 1983 | Guatemala | 1.342039 | 571.447571 | 2236.567544 | 45.891347 | 51.104597 | 35.137379 |
| 11 | 1983 | Honduras | 1.342039 | 868.445679 | 1512.185833 | 46.554395 | 50.246028 | 73.720787 |
| 12 | 1984 | El Salvador | 1.342039 | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.697311 |
| 13 | 1984 | Guatemala | 1.342039 | 578.319580 | 2189.829730 | 45.951383 | 51.025182 | 36.577950 |
| 14 | 1984 | Honduras | 1.342039 | 874.714233 | 1530.695403 | 46.363681 | 50.440498 | 54.984901 |
| 15 | 1985 | El Salvador | 1.342039 | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | 73.720787 |
| 16 | 1985 | Guatemala | 1.342039 | 597.558655 | 2121.873660 | 45.939359 | 51.010453 | 38.065460 |
| 17 | 1985 | Honduras | 1.342039 | 868.423401 | 1547.357836 | 46.190833 | 50.604823 | 73.720787 |
| 18 | 1986 | El Salvador | 1.342039 | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | 73.720787 |
| 19 | 1986 | Guatemala | 1.342039 | 626.206238 | 2073.066614 | 45.963895 | 50.941242 | 41.001839 |
| 20 | 1986 | Honduras | 1.342039 | 1516.400016 | 1512.507552 | 46.064093 | 50.698642 | 73.720787 |
| 21 | 1987 | El Salvador | 1.342039 | 1516.400016 | 2079.844180 | 41.318260 | 54.619889 | 61.982658 |
| 22 | 1987 | Guatemala | 1.342039 | 1516.400016 | 2095.342199 | 45.884100 | 50.970232 | 73.720787 |
| 23 | 1987 | Honduras | 1.342039 | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | 73.720787 |
| 24 | 1988 | El Salvador | 1.342039 | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.722801 |
| 25 | 1988 | Guatemala | 1.342039 | 1516.400016 | 2125.624163 | 45.729366 | 51.069993 | 73.720787 |
| 26 | 1988 | Honduras | 1.342039 | 930.572388 | 1581.639092 | 45.800132 | 50.875323 | 73.720787 |
| 27 | 1989 | El Salvador | 1.342039 | 1568.253540 | 2084.671422 | 40.362708 | 55.377693 | 63.612751 |
| 28 | 1989 | Guatemala | 1.342039 | 1516.400016 | 2157.313890 | 45.551915 | 51.191567 | 73.720787 |
| 29 | 1989 | Honduras | 1.342039 | 925.763672 | 1603.219717 | 45.642320 | 50.989531 | 73.720787 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.364281 |
| 79 | 2006 | Guatemala | 5.343950 | 1516.400016 | 2698.985240 | 41.024086 | 54.692058 | 75.359779 |
| 80 | 2006 | Honduras | 5.783592 | 1516.400016 | 2017.943010 | 38.938629 | 56.863273 | 88.262627 |
| 81 | 2007 | El Salvador | 18.448273 | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.298424 |
| 82 | 2007 | Guatemala | 5.077445 | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.833748 |
| 83 | 2007 | Honduras | 6.034761 | 1516.400016 | 2104.759589 | 38.204492 | 57.552528 | 73.720787 |
| 84 | 2008 | El Salvador | 18.237120 | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.281097 |
| 85 | 2008 | Guatemala | 5.240451 | 1516.400016 | 2833.735795 | 40.091781 | 55.548046 | 78.546761 |
| 86 | 2008 | Honduras | 6.339264 | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.769539 |
| 87 | 2009 | El Salvador | 19.096906 | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.675781 |
| 88 | 2009 | Guatemala | 5.539466 | 1516.400016 | 2787.128287 | 39.593279 | 55.997451 | 82.200653 |
| 89 | 2009 | Honduras | 6.338030 | 1516.400016 | 2068.185180 | 36.665394 | 58.987298 | 91.863228 |
| 90 | 2010 | El Salvador | 20.105788 | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 |
| 91 | 2010 | Guatemala | 5.639487 | 1516.400016 | 2805.951416 | 39.095628 | 56.434019 | 84.213753 |
| 92 | 2010 | Honduras | 6.964149 | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.189880 |
| 93 | 2011 | El Salvador | 20.886863 | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.104622 |
| 94 | 2011 | Guatemala | 5.653971 | 1516.400016 | 2861.167894 | 38.577533 | 56.887778 | 86.689102 |
| 95 | 2011 | Honduras | 6.437598 | 1516.400016 | 2157.984444 | 35.042579 | 60.480535 | 100.720642 |
| 96 | 2012 | El Salvador | 20.945491 | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.798729 |
| 97 | 2012 | Guatemala | 5.586203 | 1516.400016 | 2884.897429 | 38.086602 | 57.307763 | 86.083344 |
| 98 | 2012 | Honduras | 6.743448 | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.676102 |
| 99 | 2013 | El Salvador | 20.560594 | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.839989 |
| 100 | 2013 | Guatemala | 5.750461 | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.501770 |
| 101 | 2013 | Honduras | 6.798242 | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.479530 |
| 102 | 2014 | El Salvador | 21.537939 | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.617020 |
| 103 | 2014 | Guatemala | 5.716933 | 1516.400016 | 2990.594485 | 37.120959 | 58.115978 | 86.624428 |
| 104 | 2014 | Honduras | 7.389157 | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.721970 |
| 105 | 2015 | El Salvador | 22.073593 | 1516.400016 | 3853.107631 | 27.028606 | 64.799595 | 73.720787 |
| 106 | 2015 | Guatemala | 5.675817 | 1516.400016 | 3052.270569 | 36.622822 | 58.530645 | 73.720787 |
| 107 | 2015 | Honduras | 7.418273 | 1516.400016 | 2329.002149 | 31.762798 | 63.383938 | 73.720787 |
108 rows × 8 columns
migm2 = pd.merge(migm1, mig3, on=['Year', 'Country'], how='right')
migm2
| Year | Country | Migration | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | gini | income_highest% | income_lowest% | poor_1.90 | poor_3.10 | poverty_gap_1.90 | poverty_gap_3.10 | poverty_headcount_1.90 | poverty_headcount_3.10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 | 1516.400016 | 2572.813235 | 43.742478 | 52.756733 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 1 | 1980 | Guatemala | 0.886027 | 1516.400016 | 2560.782037 | 45.444923 | 51.602977 | 33.904148 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 2 | 1980 | Honduras | 1.076884 | 713.525940 | 1655.946421 | 46.957200 | 49.818337 | 44.575001 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 3 | 1981 | El Salvador | 1.342039 | 1516.400016 | 2267.095959 | 43.481122 | 52.948845 | 46.450790 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 4 | 1981 | Guatemala | 1.342039 | 493.277863 | 2509.736778 | 45.617358 | 51.409643 | 33.957420 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 5 | 1981 | Honduras | 1.342039 | 821.092712 | 1645.846419 | 46.892259 | 49.886066 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 6 | 1982 | El Salvador | 1.342039 | 999.595276 | 2092.554425 | 43.204606 | 53.154795 | 49.382709 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 7 | 1982 | Guatemala | 1.342039 | 1516.400016 | 2357.368296 | 45.771834 | 51.239138 | 33.880711 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 8 | 1982 | Honduras | 1.342039 | 864.464600 | 1573.671559 | 46.745647 | 50.043171 | 49.944939 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 9 | 1983 | El Salvador | 1.342039 | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.613579 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 10 | 1983 | Guatemala | 1.342039 | 571.447571 | 2236.567544 | 45.891347 | 51.104597 | 35.137379 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 11 | 1983 | Honduras | 1.342039 | 868.445679 | 1512.185833 | 46.554395 | 50.246028 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 12 | 1984 | El Salvador | 1.342039 | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.697311 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 13 | 1984 | Guatemala | 1.342039 | 578.319580 | 2189.829730 | 45.951383 | 51.025182 | 36.577950 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 14 | 1984 | Honduras | 1.342039 | 874.714233 | 1530.695403 | 46.363681 | 50.440498 | 54.984901 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 15 | 1985 | El Salvador | 1.342039 | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 16 | 1985 | Guatemala | 1.342039 | 597.558655 | 2121.873660 | 45.939359 | 51.010453 | 38.065460 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 17 | 1985 | Honduras | 1.342039 | 868.423401 | 1547.357836 | 46.190833 | 50.604823 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 18 | 1986 | El Salvador | 1.342039 | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 19 | 1986 | Guatemala | 1.342039 | 626.206238 | 2073.066614 | 45.963895 | 50.941242 | 41.001839 | 58.260000 | 46.730000 | 1.000000 | 4.238208 | 5.794880 | 24.99 | 39.010 | 50.940000 | 69.650000 |
| 20 | 1986 | Honduras | 1.342039 | 1516.400016 | 1512.507552 | 46.064093 | 50.698642 | 73.720787 | 55.090000 | 43.260000 | 1.230000 | 0.422176 | 0.713758 | 9.15 | 18.940 | 25.280000 | 42.740000 |
| 21 | 1987 | El Salvador | 1.342039 | 1516.400016 | 2079.844180 | 41.318260 | 54.619889 | 61.982658 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 22 | 1987 | Guatemala | 1.342039 | 1516.400016 | 2095.342199 | 45.884100 | 50.970232 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 23 | 1987 | Honduras | 1.342039 | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 24 | 1988 | El Salvador | 1.342039 | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.722801 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 25 | 1988 | Guatemala | 1.342039 | 1516.400016 | 2125.624163 | 45.729366 | 51.069993 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 26 | 1988 | Honduras | 1.342039 | 930.572388 | 1581.639092 | 45.800132 | 50.875323 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 27 | 1989 | El Salvador | 1.342039 | 1568.253540 | 2084.671422 | 40.362708 | 55.377693 | 63.612751 | 52.529231 | 40.809231 | 1.104423 | 0.983164 | 1.632736 | 11.24 | 16.660 | 18.980000 | 31.520000 |
| 28 | 1989 | Guatemala | 1.342039 | 1516.400016 | 2157.313890 | 45.551915 | 51.191567 | 73.720787 | 59.600000 | 46.780000 | 0.680000 | 3.398988 | 4.919682 | 18.71 | 29.680 | 38.020000 | 55.030000 |
| 29 | 1989 | Honduras | 1.342039 | 925.763672 | 1603.219717 | 45.642320 | 50.989531 | 73.720787 | 59.490000 | 48.180000 | 1.040000 | 1.841220 | 2.722716 | 16.90 | 29.140 | 38.600000 | 57.080000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.364281 | 45.440000 | 35.480000 | 1.790000 | 0.379692 | 1.035198 | 1.73 | 5.570 | 6.360000 | 17.340000 |
| 79 | 2006 | Guatemala | 5.343950 | 1516.400016 | 2698.985240 | 41.024086 | 54.692058 | 75.359779 | 54.890000 | 43.560000 | 1.070000 | 1.552699 | 3.195781 | 3.93 | 9.270 | 11.510000 | 23.690000 |
| 80 | 2006 | Honduras | 5.783592 | 1516.400016 | 2017.943010 | 38.938629 | 56.863273 | 88.262627 | 57.420000 | 44.050000 | 0.580000 | 1.667679 | 2.614730 | 11.40 | 18.850 | 23.790000 | 37.300000 |
| 81 | 2007 | El Salvador | 18.448273 | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.298424 | 45.240000 | 35.720000 | 1.930000 | 0.268951 | 0.835006 | 1.08 | 4.100 | 4.490000 | 13.940000 |
| 82 | 2007 | Guatemala | 5.077445 | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.833748 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 83 | 2007 | Honduras | 6.034761 | 1516.400016 | 2104.759589 | 38.204492 | 57.552528 | 73.720787 | 56.160000 | 43.810000 | 0.900000 | 1.242759 | 2.279461 | 6.91 | 13.880 | 17.430000 | 31.970000 |
| 84 | 2008 | El Salvador | 18.237120 | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.281097 | 46.650000 | 36.040000 | 1.700000 | 0.415200 | 1.114800 | 1.99 | 6.090 | 6.920000 | 18.580000 |
| 85 | 2008 | Guatemala | 5.240451 | 1516.400016 | 2833.735795 | 40.091781 | 55.548046 | 78.546761 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 86 | 2008 | Honduras | 6.339264 | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.769539 | 55.740000 | 43.870000 | 0.910000 | 1.171764 | 2.130810 | 6.30 | 12.680 | 16.140000 | 29.350000 |
| 87 | 2009 | El Salvador | 19.096906 | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.675781 | 45.930000 | 36.070000 | 1.780000 | 0.384678 | 1.054102 | 1.67 | 5.520 | 6.390000 | 17.510000 |
| 88 | 2009 | Guatemala | 5.539466 | 1516.400016 | 2787.128287 | 39.593279 | 55.997451 | 82.200653 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 89 | 2009 | Honduras | 6.338030 | 1516.400016 | 2068.185180 | 36.665394 | 58.987298 | 91.863228 | 51.560000 | 39.140000 | 1.150000 | 1.036152 | 1.979316 | 4.82 | 10.880 | 14.040000 | 26.820000 |
| 90 | 2010 | El Salvador | 20.105788 | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 | 44.530000 | 33.700000 | 1.670000 | 0.437296 | 1.120420 | 2.33 | 6.300 | 7.240000 | 18.550000 |
| 91 | 2010 | Guatemala | 5.639487 | 1516.400016 | 2805.951416 | 39.095628 | 56.434019 | 84.213753 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 92 | 2010 | Honduras | 6.964149 | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.189880 | 53.390000 | 41.020000 | 1.090000 | 1.160250 | 2.183250 | 5.40 | 11.900 | 15.470000 | 29.110000 |
| 93 | 2011 | El Salvador | 20.886863 | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.104622 | 42.430000 | 32.860000 | 2.110000 | 0.274518 | 0.911424 | 1.06 | 4.390 | 4.530000 | 15.040000 |
| 94 | 2011 | Guatemala | 5.653971 | 1516.400016 | 2861.167894 | 38.577533 | 56.887778 | 86.689102 | 52.350000 | 41.830000 | 1.340000 | 1.735265 | 3.983735 | 4.00 | 9.840 | 11.530000 | 26.470000 |
| 95 | 2011 | Honduras | 6.437598 | 1516.400016 | 2157.984444 | 35.042579 | 60.480535 | 100.720642 | 57.400000 | 45.670000 | 0.750000 | 1.428750 | 2.489454 | 7.88 | 14.660 | 18.750000 | 32.670000 |
| 96 | 2012 | El Salvador | 20.945491 | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.798729 | 41.800000 | 32.470000 | 2.150000 | 0.252512 | 0.826127 | 0.98 | 3.840 | 4.160000 | 13.610000 |
| 97 | 2012 | Guatemala | 5.586203 | 1516.400016 | 2884.897429 | 38.086602 | 57.307763 | 86.083344 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 98 | 2012 | Honduras | 6.743448 | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.676102 | 57.400000 | 45.680000 | 0.790000 | 1.653264 | 2.883924 | 9.25 | 17.100 | 21.360000 | 37.260000 |
| 99 | 2013 | El Salvador | 20.560594 | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.839989 | 43.510000 | 34.350000 | 2.110000 | 0.197925 | 0.702177 | 0.74 | 3.160 | 3.250000 | 11.530000 |
| 100 | 2013 | Guatemala | 5.750461 | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.501770 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 101 | 2013 | Honduras | 6.798242 | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.479530 | 53.670000 | 41.480000 | 0.980000 | 1.486005 | 2.712175 | 7.66 | 15.240 | 18.930000 | 34.550000 |
| 102 | 2014 | El Salvador | 21.537939 | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.617020 | 41.840000 | 32.310000 | 2.190000 | 0.181467 | 0.689819 | 0.64 | 3.000 | 2.970000 | 11.290000 |
| 103 | 2014 | Guatemala | 5.716933 | 1516.400016 | 2990.594485 | 37.120959 | 58.115978 | 86.624428 | 48.660000 | 38.360000 | 1.640000 | 1.493064 | 3.852810 | 2.72 | 8.110 | 9.320000 | 24.050000 |
| 104 | 2014 | Honduras | 7.389157 | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.721970 | 50.640000 | 38.360000 | 1.150000 | 1.270416 | 2.484316 | 6.01 | 12.970 | 15.960000 | 31.210000 |
| 105 | 2015 | El Salvador | 22.073593 | 1516.400016 | 3853.107631 | 27.028606 | 64.799595 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 106 | 2015 | Guatemala | 5.675817 | 1516.400016 | 3052.270569 | 36.622822 | 58.530645 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
| 107 | 2015 | Honduras | 7.418273 | 1516.400016 | 2329.002149 | 31.762798 | 63.383938 | 73.720787 | 52.529231 | 40.809231 | 1.104423 | 1.247831 | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 |
108 rows × 17 columns
migm3 = pd.merge(migm2, mig4, on=['Year', 'Country'], how='right')
migm3
| Year | Country | Migration | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | gini | income_highest% | ... | poor_3.10 | poverty_gap_1.90 | poverty_gap_3.10 | poverty_headcount_1.90 | poverty_headcount_3.10 | age_dependency | birth_rate | death_rate | fertility_rate | life_expectancy | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 | 1516.400016 | 2572.813235 | 43.742478 | 52.756733 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 89.549266 | 37.353 | 11.681 | 5.087 | 56.529927 |
| 1 | 1980 | Guatemala | 0.886027 | 1516.400016 | 2560.782037 | 45.444923 | 51.602977 | 33.904148 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 93.787268 | 43.686 | 11.568 | 6.195 | 57.201488 |
| 2 | 1980 | Honduras | 1.076884 | 713.525940 | 1655.946421 | 46.957200 | 49.818337 | 44.575001 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 100.729300 | 43.476 | 10.233 | 6.313 | 59.612122 |
| 3 | 1981 | El Salvador | 1.342039 | 1516.400016 | 2267.095959 | 43.481122 | 52.948845 | 46.450790 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 88.861531 | 36.593 | 11.494 | 4.952 | 56.798976 |
| 4 | 1981 | Guatemala | 1.342039 | 493.277863 | 2509.736778 | 45.617358 | 51.409643 | 33.957420 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 94.516037 | 43.384 | 11.300 | 6.161 | 57.632756 |
| 5 | 1981 | Honduras | 1.342039 | 821.092712 | 1645.846419 | 46.892259 | 49.886066 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 100.456778 | 43.020 | 9.793 | 6.190 | 60.405854 |
| 6 | 1982 | El Salvador | 1.342039 | 999.595276 | 2092.554425 | 43.204606 | 53.154795 | 49.382709 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 88.129782 | 35.833 | 11.251 | 4.819 | 57.197537 |
| 7 | 1982 | Guatemala | 1.342039 | 1516.400016 | 2357.368296 | 45.771834 | 51.239138 | 33.880711 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 95.163313 | 42.955 | 11.016 | 6.105 | 58.085951 |
| 8 | 1982 | Honduras | 1.342039 | 864.464600 | 1573.671559 | 46.745647 | 50.043171 | 49.944939 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 99.827463 | 42.524 | 9.359 | 6.062 | 61.212073 |
| 9 | 1983 | El Salvador | 1.342039 | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.613579 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 87.385786 | 35.093 | 10.953 | 4.688 | 57.731659 |
| 10 | 1983 | Guatemala | 1.342039 | 571.447571 | 2236.567544 | 45.891347 | 51.104597 | 35.137379 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 95.677113 | 42.406 | 10.718 | 6.027 | 58.558634 |
| 11 | 1983 | Honduras | 1.342039 | 868.445679 | 1512.185833 | 46.554395 | 50.246028 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 99.020708 | 41.998 | 8.933 | 5.932 | 62.024805 |
| 12 | 1984 | El Salvador | 1.342039 | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.697311 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 86.654026 | 34.383 | 10.602 | 4.562 | 58.399829 |
| 13 | 1984 | Guatemala | 1.342039 | 578.319580 | 2189.829730 | 45.951383 | 51.025182 | 36.577950 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 95.981662 | 41.757 | 10.406 | 5.930 | 59.053829 |
| 14 | 1984 | Honduras | 1.342039 | 874.714233 | 1530.695403 | 46.363681 | 50.440498 | 54.984901 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 98.253395 | 41.452 | 8.520 | 5.802 | 62.831537 |
| 15 | 1985 | El Salvador | 1.342039 | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 85.923075 | 33.716 | 10.205 | 4.442 | 59.193976 |
| 16 | 1985 | Guatemala | 1.342039 | 597.558655 | 2121.873660 | 45.939359 | 51.010453 | 38.065460 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 96.038252 | 41.053 | 10.085 | 5.820 | 59.568073 |
| 17 | 1985 | Honduras | 1.342039 | 868.423401 | 1547.357836 | 46.190833 | 50.604823 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 97.609622 | 40.898 | 8.129 | 5.674 | 63.613756 |
| 18 | 1986 | El Salvador | 1.342039 | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 84.444327 | 33.096 | 9.775 | 4.328 | 60.099854 |
| 19 | 1986 | Guatemala | 1.342039 | 626.206238 | 2073.066614 | 45.963895 | 50.941242 | 41.001839 | 58.260000 | 46.730000 | ... | 5.794880 | 24.99 | 39.010 | 50.940000 | 69.650000 | 96.304598 | 40.351 | 9.762 | 5.704 | 60.097317 |
| 20 | 1986 | Honduras | 1.342039 | 1516.400016 | 1512.507552 | 46.064093 | 50.698642 | 73.720787 | 55.090000 | 43.260000 | ... | 0.713758 | 9.15 | 18.940 | 25.280000 | 42.740000 | 97.243943 | 40.353 | 7.769 | 5.553 | 64.350951 |
| 21 | 1987 | El Salvador | 1.342039 | 1516.400016 | 2079.844180 | 41.318260 | 54.619889 | 61.982658 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 83.083490 | 32.516 | 9.330 | 4.221 | 61.074000 |
| 22 | 1987 | Guatemala | 1.342039 | 1516.400016 | 2095.342199 | 45.884100 | 50.970232 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 96.192946 | 39.705 | 9.444 | 5.591 | 60.633098 |
| 23 | 1987 | Honduras | 1.342039 | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 96.907141 | 39.826 | 7.447 | 5.439 | 65.030146 |
| 24 | 1988 | El Salvador | 1.342039 | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.722801 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 81.807159 | 31.963 | 8.893 | 4.118 | 62.071463 |
| 25 | 1988 | Guatemala | 1.342039 | 1516.400016 | 2125.624163 | 45.729366 | 51.069993 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 95.809701 | 39.153 | 9.137 | 5.488 | 61.169878 |
| 26 | 1988 | Honduras | 1.342039 | 930.572388 | 1581.639092 | 45.800132 | 50.875323 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 96.558949 | 39.319 | 7.164 | 5.333 | 65.645293 |
| 27 | 1989 | El Salvador | 1.342039 | 1568.253540 | 2084.671422 | 40.362708 | 55.377693 | 63.612751 | 52.529231 | 40.809231 | ... | 1.632736 | 11.24 | 16.660 | 18.980000 | 31.520000 | 80.578125 | 31.429 | 8.480 | 4.018 | 63.056415 |
| 28 | 1989 | Guatemala | 1.342039 | 1516.400016 | 2157.313890 | 45.551915 | 51.191567 | 73.720787 | 59.600000 | 46.780000 | ... | 4.919682 | 18.71 | 29.680 | 38.020000 | 55.030000 | 95.344674 | 38.707 | 8.845 | 5.396 | 61.704659 |
| 29 | 1989 | Honduras | 1.342039 | 925.763672 | 1603.219717 | 45.642320 | 50.989531 | 73.720787 | 59.490000 | 48.180000 | ... | 2.722716 | 16.90 | 29.140 | 38.600000 | 57.080000 | 96.118688 | 38.830 | 6.920 | 5.233 | 66.194439 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.364281 | 45.440000 | 35.480000 | ... | 1.035198 | 1.73 | 5.570 | 6.360000 | 17.340000 | 66.256872 | 19.887 | 6.672 | 2.322 | 70.479171 |
| 79 | 2006 | Guatemala | 5.343950 | 1516.400016 | 2698.985240 | 41.024086 | 54.692058 | 75.359779 | 54.890000 | 43.560000 | ... | 3.195781 | 3.93 | 9.270 | 11.510000 | 23.690000 | 82.841905 | 30.710 | 5.663 | 3.760 | 69.887902 |
| 80 | 2006 | Honduras | 5.783592 | 1516.400016 | 2017.943010 | 38.938629 | 56.863273 | 88.262627 | 57.420000 | 44.050000 | ... | 2.614730 | 11.40 | 18.850 | 23.790000 | 37.300000 | 75.860438 | 26.318 | 5.105 | 3.164 | 71.672463 |
| 81 | 2007 | El Salvador | 18.448273 | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.298424 | 45.240000 | 35.720000 | ... | 0.835006 | 1.08 | 4.100 | 4.490000 | 13.940000 | 64.944613 | 19.406 | 6.662 | 2.253 | 70.780463 |
| 82 | 2007 | Guatemala | 5.077445 | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.833748 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 81.475709 | 30.085 | 5.616 | 3.664 | 70.110780 |
| 83 | 2007 | Honduras | 6.034761 | 1516.400016 | 2104.759589 | 38.204492 | 57.552528 | 73.720787 | 56.160000 | 43.810000 | ... | 2.279461 | 6.91 | 13.880 | 17.430000 | 31.970000 | 73.754313 | 25.514 | 5.077 | 3.039 | 71.858732 |
| 84 | 2008 | El Salvador | 18.237120 | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.281097 | 46.650000 | 36.040000 | ... | 1.114800 | 1.99 | 6.090 | 6.920000 | 18.580000 | 63.639250 | 18.969 | 6.659 | 2.189 | 71.080780 |
| 85 | 2008 | Guatemala | 5.240451 | 1516.400016 | 2833.735795 | 40.091781 | 55.548046 | 78.546761 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 80.024334 | 29.519 | 5.576 | 3.578 | 70.328146 |
| 86 | 2008 | Honduras | 6.339264 | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.769539 | 55.740000 | 43.870000 | ... | 2.130810 | 6.30 | 12.680 | 16.140000 | 29.350000 | 71.645904 | 24.728 | 5.055 | 2.918 | 72.039976 |
| 87 | 2009 | El Salvador | 19.096906 | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.675781 | 45.930000 | 36.070000 | ... | 1.054102 | 1.67 | 5.520 | 6.390000 | 17.510000 | 62.302599 | 18.574 | 6.663 | 2.130 | 71.378146 |
| 88 | 2009 | Guatemala | 5.539466 | 1516.400016 | 2787.128287 | 39.593279 | 55.997451 | 82.200653 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 78.579556 | 29.016 | 5.539 | 3.501 | 70.547537 |
| 89 | 2009 | Honduras | 6.338030 | 1516.400016 | 2068.185180 | 36.665394 | 58.987298 | 91.863228 | 51.560000 | 39.140000 | ... | 1.979316 | 4.82 | 10.880 | 14.040000 | 26.820000 | 69.528024 | 23.971 | 5.038 | 2.802 | 72.217220 |
| 90 | 2010 | El Salvador | 20.105788 | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 | 44.530000 | 33.700000 | ... | 1.120420 | 2.33 | 6.300 | 7.240000 | 18.550000 | 60.931929 | 18.223 | 6.673 | 2.078 | 71.670610 |
| 91 | 2010 | Guatemala | 5.639487 | 1516.400016 | 2805.951416 | 39.095628 | 56.434019 | 84.213753 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 77.198083 | 28.574 | 5.503 | 3.434 | 70.775463 |
| 92 | 2010 | Honduras | 6.964149 | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.189880 | 53.390000 | 41.020000 | ... | 2.183250 | 5.40 | 11.900 | 15.470000 | 29.110000 | 67.411337 | 23.261 | 5.026 | 2.695 | 72.393976 |
| 93 | 2011 | El Salvador | 20.886863 | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.104622 | 42.430000 | 32.860000 | ... | 0.911424 | 1.06 | 4.390 | 4.530000 | 15.040000 | 59.472471 | 17.924 | 6.692 | 2.031 | 71.956171 |
| 94 | 2011 | Guatemala | 5.653971 | 1516.400016 | 2861.167894 | 38.577533 | 56.887778 | 86.689102 | 52.350000 | 41.830000 | ... | 3.983735 | 4.00 | 9.840 | 11.530000 | 26.470000 | 75.784683 | 28.182 | 5.467 | 3.373 | 71.010415 |
| 95 | 2011 | Honduras | 6.437598 | 1516.400016 | 2157.984444 | 35.042579 | 60.480535 | 100.720642 | 57.400000 | 45.670000 | ... | 2.489454 | 7.88 | 14.660 | 18.750000 | 32.670000 | 65.342451 | 22.622 | 5.017 | 2.599 | 72.572732 |
| 96 | 2012 | El Salvador | 20.945491 | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.798729 | 41.800000 | 32.470000 | ... | 0.826127 | 0.98 | 3.840 | 4.160000 | 13.610000 | 57.970629 | 17.676 | 6.718 | 1.991 | 72.231854 |
| 97 | 2012 | Guatemala | 5.586203 | 1516.400016 | 2884.897429 | 38.086602 | 57.307763 | 86.083344 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 74.496429 | 27.819 | 5.433 | 3.317 | 71.249390 |
| 98 | 2012 | Honduras | 6.743448 | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.676102 | 57.400000 | 45.680000 | ... | 2.883924 | 9.25 | 17.100 | 21.360000 | 37.260000 | 63.264970 | 22.065 | 5.012 | 2.514 | 72.755024 |
| 99 | 2013 | El Salvador | 20.560594 | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.839989 | 43.510000 | 34.350000 | ... | 0.702177 | 0.74 | 3.160 | 3.250000 | 11.530000 | 56.531230 | 17.476 | 6.751 | 1.958 | 72.498146 |
| 100 | 2013 | Guatemala | 5.750461 | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.501770 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 73.279326 | 27.465 | 5.401 | 3.263 | 71.486390 |
| 101 | 2013 | Honduras | 6.798242 | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.479530 | 53.670000 | 41.480000 | ... | 2.712175 | 7.66 | 15.240 | 18.930000 | 34.550000 | 61.250546 | 21.593 | 5.010 | 2.442 | 72.942854 |
| 102 | 2014 | El Salvador | 21.537939 | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.617020 | 41.840000 | 32.310000 | ... | 0.689819 | 0.64 | 3.000 | 2.970000 | 11.290000 | 55.294848 | 17.314 | 6.790 | 1.931 | 72.754561 |
| 103 | 2014 | Guatemala | 5.716933 | 1516.400016 | 2990.594485 | 37.120959 | 58.115978 | 86.624428 | 48.660000 | 38.360000 | ... | 3.852810 | 2.72 | 8.110 | 9.320000 | 24.050000 | 72.069718 | 27.112 | 5.370 | 3.211 | 71.722415 |
| 104 | 2014 | Honduras | 7.389157 | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.721970 | 50.640000 | 38.360000 | ... | 2.484316 | 6.01 | 12.970 | 15.960000 | 31.210000 | 59.400971 | 21.203 | 5.011 | 2.382 | 73.135707 |
| 105 | 2015 | El Salvador | 22.073593 | 1516.400016 | 3853.107631 | 27.028606 | 64.799595 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 54.321951 | 17.175 | 6.833 | 1.909 | 73.001098 |
| 106 | 2015 | Guatemala | 5.675817 | 1516.400016 | 3052.270569 | 36.622822 | 58.530645 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 70.850672 | 26.752 | 5.339 | 3.159 | 71.956488 |
| 107 | 2015 | Honduras | 7.418273 | 1516.400016 | 2329.002149 | 31.762798 | 63.383938 | 73.720787 | 52.529231 | 40.809231 | ... | 2.223817 | 8.20 | 15.195 | 18.977037 | 33.377593 | 57.768677 | 20.881 | 5.015 | 2.332 | 73.333122 |
108 rows × 22 columns
migration_flows = pd.merge(migm3, mig6, on=['Year', 'Country'], how='right')
migration_flows
| Year | Country | Migration | enrolment_tertiary | GDP_percapita_constant | pop_ages_0-14% | pop_ages_14-64% | primary_completion | gini | income_highest% | ... | trade | short_term_debt | pop_growth | remittances | net_bilateral_aid | imports_%GDP | gov_consumption | FDI | exports_%GDP | employment_15+ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | El Salvador | 2.063205 | 1516.400016 | 2572.813235 | 43.742478 | 52.756733 | 73.720787 | 52.529231 | 40.809231 | ... | 67.406464 | 22.0700 | 1.739184 | 1.372147 | 43000000 | 33.244917 | 13.988965 | 0.014116 | 34.161547 | 51.230213 |
| 1 | 1980 | Guatemala | 0.886027 | 1516.400016 | 2560.782037 | 45.444923 | 51.602977 | 33.904148 | 52.529231 | 40.809231 | ... | 47.105487 | 26.3830 | 2.635143 | 0.332542 | 17000000 | 24.919086 | 7.958166 | 0.025385 | 22.186401 | 49.610001 |
| 2 | 1980 | Honduras | 1.076884 | 713.525940 | 1655.946421 | 46.957200 | 49.818337 | 44.575001 | 52.529231 | 40.809231 | ... | 81.293839 | 17.3081 | 3.145300 | 0.062354 | 19000000 | 44.056895 | 12.665627 | 0.038971 | 37.236944 | 27.379999 |
| 3 | 1981 | El Salvador | 1.342039 | 1516.400016 | 2267.095959 | 43.481122 | 52.948845 | 46.450790 | 52.529231 | 40.809231 | ... | 60.266492 | 17.3809 | 1.611673 | 2.108693 | 97000000 | 33.587802 | 15.829162 | 0.014116 | 26.678690 | 51.230213 |
| 4 | 1981 | Guatemala | 1.342039 | 493.277863 | 2509.736778 | 45.617358 | 51.409643 | 33.957420 | 52.529231 | 40.809231 | ... | 40.691257 | 9.9649 | 2.658257 | 0.284635 | 18000000 | 23.601509 | 7.900087 | -0.011618 | 17.089748 | 51.230213 |
| 5 | 1981 | Honduras | 1.342039 | 821.092712 | 1645.846419 | 46.892259 | 49.886066 | 73.720787 | 52.529231 | 40.809231 | ... | 69.338535 | 13.3695 | 3.113439 | 0.062068 | 35000000 | 37.701720 | 12.785955 | 0.070935 | 31.636815 | 26.430000 |
| 6 | 1982 | El Salvador | 1.342039 | 999.595276 | 2092.554425 | 43.204606 | 53.154795 | 49.382709 | 52.529231 | 40.809231 | ... | 51.247740 | 13.4222 | 1.498034 | 3.300787 | 170000000 | 28.470337 | 15.778234 | 0.014116 | 22.777403 | 51.230213 |
| 7 | 1982 | Guatemala | 1.342039 | 1516.400016 | 2357.368296 | 45.771834 | 51.239138 | 33.880711 | 52.529231 | 40.809231 | ... | 33.474818 | 7.2350 | 2.669298 | 0.122749 | 20000000 | 18.687621 | 7.743490 | -0.045887 | 14.787197 | 51.230213 |
| 8 | 1982 | Honduras | 1.342039 | 864.464600 | 1573.671559 | 46.745647 | 50.043171 | 49.944939 | 52.529231 | 40.809231 | ... | 54.727051 | 7.7859 | 3.083349 | 0.051662 | 68000000 | 28.052348 | 13.053211 | -0.034441 | 26.674702 | 40.090000 |
| 9 | 1983 | El Salvador | 1.342039 | 1203.906616 | 2094.864582 | 42.920848 | 53.365841 | 50.613579 | 52.529231 | 40.809231 | ... | 54.397801 | 5.1284 | 1.413014 | 3.292315 | 231000000 | 29.909283 | 15.829448 | 0.014116 | 24.488518 | 51.230213 |
| 10 | 1983 | Guatemala | 1.342039 | 571.447571 | 2236.567544 | 45.891347 | 51.104597 | 35.137379 | 52.529231 | 40.809231 | ... | 27.546959 | 6.1899 | 2.654604 | 0.043094 | 36000000 | 14.552484 | 7.602210 | 0.000000 | 12.994475 | 51.230213 |
| 11 | 1983 | Honduras | 1.342039 | 868.445679 | 1512.185833 | 46.554395 | 50.246028 | 73.720787 | 52.529231 | 40.809231 | ... | 55.394868 | 6.3325 | 3.056878 | 0.058499 | 64000000 | 29.233021 | 13.113422 | 0.064998 | 26.161846 | 35.599998 |
| 12 | 1984 | El Salvador | 1.342039 | 1310.496826 | 2094.098791 | 42.636120 | 53.575056 | 48.697311 | 52.529231 | 40.809231 | ... | 50.290675 | 5.8131 | 1.364660 | 4.345542 | 221000000 | 28.536536 | 16.036198 | 0.014116 | 21.754139 | 51.230213 |
| 13 | 1984 | Guatemala | 1.342039 | 578.319580 | 2189.829730 | 45.951383 | 51.025182 | 36.577950 | 52.529231 | 40.809231 | ... | 28.153115 | 6.1022 | 2.607323 | 0.035903 | 29000000 | 15.151003 | 7.665259 | 0.052798 | 13.002112 | 51.230213 |
| 14 | 1984 | Honduras | 1.342039 | 874.714233 | 1530.695403 | 46.363681 | 50.440498 | 54.984901 | 52.529231 | 40.809231 | ... | 57.728231 | 8.2600 | 3.037319 | 0.058753 | 123000000 | 32.027719 | 13.196746 | -0.030130 | 25.700512 | 38.720001 |
| 15 | 1985 | El Salvador | 1.342039 | 1439.984375 | 2078.900486 | 42.346470 | 53.785685 | 73.720787 | 52.529231 | 40.809231 | ... | 52.210538 | 4.3227 | 1.343117 | 4.135388 | 287000000 | 29.886772 | 15.490521 | 0.014116 | 22.323766 | 51.230213 |
| 16 | 1985 | Guatemala | 1.342039 | 597.558655 | 2121.873660 | 45.939359 | 51.010453 | 38.065460 | 52.529231 | 40.809231 | ... | 24.932246 | 9.5640 | 2.541226 | 0.010286 | 50000000 | 12.984016 | 6.953551 | 0.000000 | 11.948230 | 51.299999 |
| 17 | 1985 | Honduras | 1.342039 | 868.423401 | 1547.357836 | 46.190833 | 50.604823 | 73.720787 | 52.529231 | 40.809231 | ... | 54.966344 | 10.6047 | 3.020231 | 0.057700 | 161000000 | 29.866742 | 13.092458 | 0.000000 | 25.099602 | 51.230213 |
| 18 | 1986 | El Salvador | 1.342039 | 1492.553833 | 2055.438830 | 41.819358 | 54.216902 | 73.720787 | 52.529231 | 40.809231 | ... | 53.714123 | 6.5223 | 1.324193 | 4.170338 | 272000000 | 29.045960 | 14.181097 | 0.014116 | 24.668162 | 51.230213 |
| 19 | 1986 | Guatemala | 1.342039 | 626.206238 | 2073.066614 | 45.963895 | 50.941242 | 41.001839 | 58.260000 | 46.730000 | ... | 30.644019 | 10.7110 | 2.470001 | 0.009679 | 86000000 | 14.592119 | 7.096224 | 0.000000 | 16.051900 | 51.230213 |
| 20 | 1986 | Honduras | 1.342039 | 1516.400016 | 1512.507552 | 46.064093 | 50.698642 | 73.720787 | 55.090000 | 43.260000 | ... | 54.890376 | 11.6717 | 2.998073 | 0.055140 | 175000000 | 28.305107 | 14.270711 | 0.000000 | 26.585269 | 51.230213 |
| 21 | 1987 | El Salvador | 1.342039 | 1516.400016 | 2079.844180 | 41.318260 | 54.619889 | 61.982658 | 52.529231 | 40.809231 | ... | 45.094622 | 10.3707 | 1.302156 | 4.715458 | 356000000 | 26.102318 | 13.745705 | 0.014116 | 18.992304 | 51.230213 |
| 22 | 1987 | Guatemala | 1.342039 | 1516.400016 | 2095.342199 | 45.884100 | 50.970232 | 73.720787 | 52.529231 | 40.809231 | ... | 38.142963 | 9.8328 | 2.413838 | 0.001412 | 155000000 | 22.294055 | 7.902434 | 0.014116 | 15.848908 | 51.230213 |
| 23 | 1987 | Honduras | 1.342039 | 835.494751 | 1556.855276 | 45.935714 | 50.785360 | 73.720787 | 52.529231 | 40.809231 | ... | 48.789886 | 11.9562 | 2.966596 | 0.811559 | 153000000 | 25.827815 | 14.220348 | 0.024082 | 22.962071 | 51.230213 |
| 24 | 1988 | El Salvador | 1.342039 | 1515.808716 | 2091.693100 | 40.835477 | 55.003335 | 65.722801 | 52.529231 | 40.809231 | ... | 38.095704 | 11.6370 | 1.291964 | 5.029738 | 318000000 | 22.285022 | 12.731673 | 0.014116 | 15.810682 | 51.230213 |
| 25 | 1988 | Guatemala | 1.342039 | 1516.400016 | 2125.624163 | 45.729366 | 51.069993 | 73.720787 | 52.529231 | 40.809231 | ... | 38.039914 | 11.0787 | 2.383684 | 0.582789 | 134000000 | 21.936239 | 7.981505 | -0.012752 | 16.103675 | 51.230213 |
| 26 | 1988 | Honduras | 1.342039 | 930.572388 | 1581.639092 | 45.800132 | 50.875323 | 73.720787 | 52.529231 | 40.809231 | ... | 55.215652 | 12.1001 | 2.927378 | 1.052794 | 155000000 | 28.926603 | 14.139012 | -0.025186 | 26.289050 | 51.230213 |
| 27 | 1989 | El Salvador | 1.342039 | 1568.253540 | 2084.671422 | 40.362708 | 55.377693 | 63.612751 | 52.529231 | 40.809231 | ... | 36.928296 | 9.7005 | 1.293850 | 5.439348 | 310000000 | 23.690508 | 12.194134 | 0.014116 | 13.237788 | 51.230213 |
| 28 | 1989 | Guatemala | 1.342039 | 1516.400016 | 2157.313890 | 45.551915 | 51.191567 | 73.720787 | 59.600000 | 46.780000 | ... | 39.781546 | 12.1191 | 2.387014 | 1.010615 | 146000000 | 22.474097 | 7.895426 | -0.047558 | 17.307449 | 51.230213 |
| 29 | 1989 | Honduras | 1.342039 | 925.763672 | 1603.219717 | 45.642320 | 50.989531 | 73.720787 | 59.490000 | 48.180000 | ... | 65.347396 | 11.4579 | 2.879679 | 1.363847 | 102000000 | 34.342945 | 14.273274 | -0.028063 | 31.004451 | 51.230213 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2006 | El Salvador | 17.546949 | 2093.922607 | 3475.866745 | 33.230254 | 60.147890 | 92.364281 | 45.440000 | 35.480000 | ... | 71.849041 | 12.6699 | 0.341593 | 18.773955 | 24540000 | 46.166991 | 9.826583 | -0.141774 | 25.682050 | 49.169998 |
| 79 | 2006 | Guatemala | 5.343950 | 1516.400016 | 2698.985240 | 41.024086 | 54.692058 | 75.359779 | 54.890000 | 43.560000 | ... | 66.818187 | 13.4658 | 2.298528 | 12.239366 | 67250000 | 41.886457 | 8.369963 | 0.276205 | 24.931730 | 57.549999 |
| 80 | 2006 | Honduras | 5.783592 | 1516.400016 | 2017.943010 | 38.938629 | 56.863273 | 88.262627 | 57.420000 | 44.050000 | ... | 133.131835 | 7.1408 | 1.826883 | 21.557383 | 84100000 | 77.077193 | 15.003781 | 0.452738 | 56.054642 | 49.080002 |
| 81 | 2007 | El Salvador | 18.448273 | 2209.102051 | 3597.961991 | 32.566759 | 60.626412 | 97.298424 | 45.240000 | 35.720000 | ... | 74.177439 | 13.1110 | 0.315511 | 18.448488 | 39040000 | 48.294694 | 9.281817 | 0.473516 | 25.882745 | 49.500000 |
| 82 | 2007 | Guatemala | 5.077445 | 1695.110107 | 2805.169791 | 40.575993 | 55.103794 | 75.833748 | 52.529231 | 40.809231 | ... | 67.898497 | 15.8088 | 2.254506 | 12.418103 | 45710000 | 42.333233 | 8.658056 | 0.408934 | 25.565264 | 51.230213 |
| 83 | 2007 | Honduras | 6.034761 | 1516.400016 | 2104.759589 | 38.204492 | 57.552528 | 73.720787 | 56.160000 | 43.810000 | ... | 135.070635 | 10.5623 | 1.792143 | 21.291564 | 71100000 | 81.561623 | 16.602374 | 0.332742 | 53.509012 | 51.230213 |
| 84 | 2008 | El Salvador | 18.237120 | 2308.634277 | 3633.014903 | 31.905088 | 61.110033 | 99.281097 | 46.650000 | 36.040000 | ... | 76.580188 | 14.4812 | 0.296649 | 17.520181 | 42370000 | 49.698567 | 9.175027 | 0.370631 | 26.881620 | 59.020000 |
| 85 | 2008 | Guatemala | 5.240451 | 1516.400016 | 2833.735795 | 40.091781 | 55.548046 | 78.546761 | 52.529231 | 40.809231 | ... | 64.125228 | 15.5632 | 2.215217 | 11.395396 | 70350000 | 39.406974 | 9.013268 | 0.034880 | 24.718254 | 51.230213 |
| 86 | 2008 | Honduras | 6.339264 | 2035.134766 | 2155.827865 | 37.448905 | 58.259473 | 88.769539 | 55.740000 | 43.870000 | ... | 135.748955 | 13.3425 | 1.747160 | 20.459753 | 96330000 | 84.423679 | 17.114828 | 1.402889 | 51.325277 | 51.230213 |
| 87 | 2009 | El Salvador | 19.096906 | 2388.975342 | 3509.156436 | 31.228684 | 61.613308 | 102.675781 | 45.930000 | 36.070000 | ... | 61.871642 | 8.3258 | 0.285542 | 16.467451 | 82080000 | 38.674798 | 10.629205 | 0.014327 | 23.196844 | 58.160000 |
| 88 | 2009 | Guatemala | 5.539466 | 1516.400016 | 2787.128287 | 39.593279 | 55.997451 | 82.200653 | 52.529231 | 40.809231 | ... | 57.105993 | 8.2714 | 2.183077 | 10.651859 | 83890000 | 33.130589 | 10.179023 | 0.325371 | 23.975404 | 51.230213 |
| 89 | 2009 | Honduras | 6.338030 | 1516.400016 | 2068.185180 | 36.665394 | 58.987298 | 91.863228 | 51.560000 | 39.140000 | ... | 96.905006 | 6.5494 | 1.688651 | 17.101447 | 128760000 | 57.374755 | 18.699738 | -0.074317 | 39.530251 | 59.320000 |
| 90 | 2010 | El Salvador | 20.105788 | 2484.339111 | 3547.070983 | 30.534690 | 62.138073 | 105.430397 | 44.530000 | 33.700000 | ... | 68.768763 | 7.5574 | 0.280903 | 16.209675 | 148160000 | 42.844203 | 10.704865 | 0.524807 | 25.924560 | 58.110001 |
| 91 | 2010 | Guatemala | 5.639487 | 1516.400016 | 2805.951416 | 39.095628 | 56.434019 | 84.213753 | 52.529231 | 40.809231 | ... | 62.114932 | 10.5394 | 2.156000 | 10.236867 | 100500000 | 36.309182 | 10.475864 | 0.153036 | 25.805750 | 42.919998 |
| 92 | 2010 | Honduras | 6.964149 | 2263.870361 | 2110.822021 | 35.854009 | 59.733111 | 97.189880 | 53.390000 | 41.020000 | ... | 109.441838 | 9.4110 | 1.622622 | 16.643140 | 100840000 | 63.682932 | 17.926432 | -2.309012 | 45.758906 | 59.360001 |
| 93 | 2011 | El Salvador | 20.886863 | 2648.530029 | 3615.583230 | 29.801800 | 62.706748 | 109.104622 | 42.430000 | 32.860000 | ... | 74.643243 | 10.1925 | 0.279522 | 15.748084 | 162440000 | 46.663209 | 11.055793 | -0.414578 | 27.980034 | 58.549999 |
| 94 | 2011 | Guatemala | 5.653971 | 1516.400016 | 2861.167894 | 38.577533 | 56.887778 | 86.689102 | 52.350000 | 41.830000 | ... | 63.984196 | 13.9156 | 2.129043 | 9.492785 | 93080000 | 37.358771 | 10.189228 | 0.274376 | 26.625425 | 59.230000 |
| 95 | 2011 | Honduras | 6.437598 | 1516.400016 | 2157.984444 | 35.042579 | 60.480535 | 100.720642 | 57.400000 | 45.670000 | ... | 122.216903 | 6.0942 | 1.554236 | 15.980083 | 46360000 | 70.959216 | 16.064368 | 0.172124 | 51.257687 | 49.660000 |
| 96 | 2012 | El Salvador | 20.945491 | 2797.323486 | 3673.262887 | 29.042627 | 63.302907 | 108.798729 | 41.800000 | 32.470000 | ... | 69.698828 | 11.7092 | 0.280768 | 16.361001 | 150850000 | 44.076494 | 11.228458 | -0.150729 | 25.622333 | 59.400002 |
| 97 | 2012 | Guatemala | 5.586203 | 1516.400016 | 2884.897429 | 38.086602 | 57.307763 | 86.083344 | 52.529231 | 40.809231 | ... | 60.982475 | 4.5907 | 2.100666 | 9.983742 | 95490000 | 36.113781 | 10.347731 | 0.115398 | 24.868694 | 63.540001 |
| 98 | 2012 | Honduras | 6.743448 | 2261.272461 | 2213.759527 | 34.200196 | 61.250126 | 100.676102 | 57.400000 | 45.680000 | ... | 121.188216 | 7.9444 | 1.493978 | 15.871110 | 52650000 | 70.285417 | 16.201783 | 1.177095 | 50.902799 | 51.230213 |
| 99 | 2013 | El Salvador | 20.560594 | 2891.187012 | 3730.422292 | 28.295414 | 63.885015 | 106.839989 | 43.510000 | 34.350000 | ... | 71.948881 | 13.6861 | 0.286321 | 16.241404 | 51090000 | 45.577371 | 11.564254 | 0.271335 | 26.371510 | 59.880001 |
| 100 | 2013 | Guatemala | 5.750461 | 1871.932129 | 2930.170750 | 37.607424 | 57.710289 | 86.501770 | 52.529231 | 40.809231 | ... | 58.548341 | 4.1360 | 2.073729 | 9.988782 | 102670000 | 34.828649 | 10.570013 | 0.169948 | 23.719693 | 58.340000 |
| 101 | 2013 | Honduras | 6.798242 | 2340.688232 | 2242.818455 | 33.349845 | 62.015294 | 94.479530 | 53.670000 | 41.480000 | ... | 116.306049 | 7.4932 | 1.449196 | 16.863760 | 90910000 | 68.364559 | 16.733813 | 0.421417 | 47.941490 | 51.580002 |
| 102 | 2014 | El Salvador | 21.537939 | 2886.402832 | 3772.401570 | 27.615213 | 64.393636 | 104.617020 | 41.840000 | 32.310000 | ... | 69.570771 | 13.8859 | 0.296163 | 16.567886 | 45370000 | 43.700058 | 11.522619 | 0.791026 | 25.870712 | 51.230213 |
| 103 | 2014 | Guatemala | 5.716933 | 1516.400016 | 2990.594485 | 37.120959 | 58.115978 | 86.624428 | 48.660000 | 38.360000 | ... | 56.717915 | 3.9421 | 2.048252 | 9.941701 | 126040000 | 33.559497 | 10.848533 | -0.198735 | 23.158419 | 58.410000 |
| 104 | 2014 | Honduras | 7.389157 | 2334.632813 | 2279.309902 | 32.529328 | 62.734875 | 90.721970 | 50.640000 | 38.360000 | ... | 112.609235 | 6.8729 | 1.424638 | 17.385695 | 80450000 | 65.739895 | 15.710074 | 0.898223 | 46.869340 | 51.230213 |
| 105 | 2015 | El Salvador | 22.073593 | 1516.400016 | 3853.107631 | 27.028606 | 64.799595 | 73.720787 | 52.529231 | 40.809231 | ... | 67.989029 | 13.4668 | 0.308592 | 16.577147 | 47470000 | 42.030236 | 11.893138 | 0.346951 | 25.958793 | 51.230213 |
| 106 | 2015 | Guatemala | 5.675817 | 1516.400016 | 3052.270569 | 36.622822 | 58.530645 | 73.720787 | 52.529231 | 40.809231 | ... | 51.333403 | 3.6161 | 2.023674 | 10.303115 | 123500000 | 30.043441 | 10.365617 | 0.049519 | 21.289963 | 58.900002 |
| 107 | 2015 | Honduras | 7.418273 | 1516.400016 | 2329.002149 | 31.762798 | 63.383938 | 73.720787 | 52.529231 | 40.809231 | ... | 107.434916 | 6.4723 | 1.414027 | 17.953123 | 110380000 | 62.588715 | 14.704325 | 0.998200 | 44.846202 | 60.630001 |
108 rows × 33 columns
migration_flows.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 108 entries, 0 to 107 Data columns (total 33 columns): Year 108 non-null int64 Country 108 non-null object Migration 108 non-null float64 enrolment_tertiary 108 non-null float64 GDP_percapita_constant 108 non-null float64 pop_ages_0-14% 108 non-null float64 pop_ages_14-64% 108 non-null float64 primary_completion 108 non-null float64 gini 108 non-null float64 income_highest% 108 non-null float64 income_lowest% 108 non-null float64 poor_1.90 108 non-null float64 poor_3.10 108 non-null float64 poverty_gap_1.90 108 non-null float64 poverty_gap_3.10 108 non-null float64 poverty_headcount_1.90 108 non-null float64 poverty_headcount_3.10 108 non-null float64 age_dependency 108 non-null float64 birth_rate 108 non-null float64 death_rate 108 non-null float64 fertility_rate 108 non-null float64 life_expectancy 108 non-null float64 unemployment 108 non-null float64 trade 108 non-null float64 short_term_debt 108 non-null float64 pop_growth 108 non-null float64 remittances 108 non-null float64 net_bilateral_aid 108 non-null int64 imports_%GDP 108 non-null float64 gov_consumption 108 non-null float64 FDI 108 non-null float64 exports_%GDP 108 non-null float64 employment_15+ 108 non-null float64 dtypes: float64(30), int64(2), object(1) memory usage: 28.7+ KB
fig = plt.figure(figsize=(18,10))
ax = fig.gca()
sns.heatmap(migration_flows.corr(), annot=True, linewidths=.15, cmap="YlGnBu", vmin=0, vmax=1, ax=ax)
plt.show()
migration_flows.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 108 entries, 0 to 107 Data columns (total 33 columns): Year 108 non-null int64 Country 108 non-null object Migration 108 non-null float64 enrolment_tertiary 108 non-null float64 GDP_percapita_constant 108 non-null float64 pop_ages_0-14% 108 non-null float64 pop_ages_14-64% 108 non-null float64 primary_completion 108 non-null float64 gini 108 non-null float64 income_highest% 108 non-null float64 income_lowest% 108 non-null float64 poor_1.90 108 non-null float64 poor_3.10 108 non-null float64 poverty_gap_1.90 108 non-null float64 poverty_gap_3.10 108 non-null float64 poverty_headcount_1.90 108 non-null float64 poverty_headcount_3.10 108 non-null float64 age_dependency 108 non-null float64 birth_rate 108 non-null float64 death_rate 108 non-null float64 fertility_rate 108 non-null float64 life_expectancy 108 non-null float64 unemployment 108 non-null float64 trade 108 non-null float64 short_term_debt 108 non-null float64 pop_growth 108 non-null float64 remittances 108 non-null float64 net_bilateral_aid 108 non-null int64 imports_%GDP 108 non-null float64 gov_consumption 108 non-null float64 FDI 108 non-null float64 exports_%GDP 108 non-null float64 employment_15+ 108 non-null float64 dtypes: float64(30), int64(2), object(1) memory usage: 28.7+ KB
del migration_flows['enrolment_tertiary']
del migration_flows['pop_ages_0-14%']
del migration_flows['pop_ages_14-64%']
del migration_flows['primary_completion']
del migration_flows['gini']
del migration_flows['poor_1.90']
del migration_flows['poor_3.10']
del migration_flows['poverty_gap_1.90']
del migration_flows['poverty_gap_3.10']
del migration_flows['poverty_headcount_3.10']
del migration_flows['age_dependency']
del migration_flows['birth_rate']
del migration_flows['fertility_rate']
del migration_flows['life_expectancy']
del migration_flows['short_term_debt']
del migration_flows['gov_consumption']
del migration_flows['employment_15+']
del migration_flows['imports_%GDP']
del migration_flows['exports_%GDP']
fig = plt.figure(figsize=(18,10))
ax = fig.gca()
sns.heatmap(migration_flows.corr(), annot=True, linewidths=.15, cmap="YlGnBu", vmin=0, vmax=1, ax=ax)
plt.show()
migration_flows.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 108 entries, 0 to 107 Data columns (total 14 columns): Year 108 non-null datetime64[ns] Country 108 non-null object Migration 108 non-null float64 GDP_percapita_constant 108 non-null float64 income_highest% 108 non-null float64 income_lowest% 108 non-null float64 poverty_headcount_1.90 108 non-null float64 death_rate 108 non-null float64 unemployment 108 non-null float64 trade 108 non-null float64 pop_growth 108 non-null float64 remittances 108 non-null float64 net_bilateral_aid 108 non-null int64 FDI 108 non-null float64 dtypes: datetime64[ns](1), float64(11), int64(1), object(1) memory usage: 12.7+ KB
migration_flows['Year'] = pd.to_datetime(migration_flows['Year'])
migration_flows.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 108 entries, 0 to 107 Data columns (total 14 columns): Year 108 non-null datetime64[ns] Country 108 non-null object Migration 108 non-null float64 GDP_percapita_constant 108 non-null float64 income_highest% 108 non-null float64 income_lowest% 108 non-null float64 poverty_headcount_1.90 108 non-null float64 death_rate 108 non-null float64 unemployment 108 non-null float64 trade 108 non-null float64 pop_growth 108 non-null float64 remittances 108 non-null float64 net_bilateral_aid 108 non-null int64 FDI 108 non-null float64 dtypes: datetime64[ns](1), float64(11), int64(1), object(1) memory usage: 12.7+ KB
fig = plt.figure(figsize=(18,10))
ax = fig.gca()
sns.heatmap(migration_flows.corr(), annot=True, linewidths=.15, cmap="YlGnBu", vmin=0, vmax=1, ax=ax)
plt.show()
sns.pairplot(migration_flows, hue="Country", plot_kws={"s": 25}, size = 3)
<seaborn.axisgrid.PairGrid at 0x13d861850>
y = migration_flows.Migration.values
x = migration_flows[['GDP_percapita_constant', 'income_highest%', 'income_lowest%', 'poverty_headcount_1.90', 'death_rate', 'unemployment', 'trade', 'pop_growth', 'remittances', 'net_bilateral_aid', 'FDI']]
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
Xn = ss.fit_transform(x)
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(Xn, y, test_size=0.3, random_state=10)
print x_train.shape, x_test.shape
print "\n======\n"
print y_train.shape, y_test.shape
(75, 11) (33, 11) ====== (75,) (33,)
from sklearn.linear_model import LinearRegression
## define a linear regression model
lr = LinearRegression()
## fit your model
lr.fit(x_train, y_train)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
''' Function that calls the MSE and R^2 at once, using the name of the method and calling the best model'''
def rsquare_meansquare_error(train_y, test_y, train_x, test_x, test, best_model):
""" first we need to predict on the test and train data"""
y_train_pred = best_model.predict(train_x)
y_test_pred = best_model.predict(test_x)
""" We call the MSE in the following lines"""
print ('MSE ' + test + ' train data: %.2f, test data: %.2f' % (
mean_squared_error(train_y, y_train_pred),
mean_squared_error(test_y, y_test_pred)))
""" We call the R^2 in the following lines"""
print('R^2 ' + test + ' train data: %.2f, test data: %.2f' % (
r2_score(train_y, y_train_pred),
r2_score(test_y, y_test_pred)))
rsquare_meansquare_error(y_train, y_test, x_train, x_test, "OLS", lr)
MSE OLS train data: 4.49, test data: 5.22 R^2 OLS train data: 0.85, test data: 0.72
from sklearn.linear_model import Ridge, Lasso, ElasticNet, RidgeCV, LassoCV, ElasticNetCV
## Find the optimal alpha
ridge_alphas = np.logspace(0, 5, 100)
optimal_ridge = RidgeCV(alphas=ridge_alphas, cv=10)
optimal_ridge.fit(x_train, y_train)
print (optimal_ridge.alpha_)
11.497569954
## Implement the Ridge Regression
ridge = Ridge(alpha=optimal_ridge.alpha_)
## Fit the Ridge regression
ridge.fit(x_train, y_train)
Ridge(alpha=11.497569953977356, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001)
## Evaluate the Ridge Regression
rsquare_meansquare_error(y_train, y_test, x_train, x_test, "Ridge", ridge)
MSE Ridge train data: 4.84, test data: 5.37 R^2 Ridge train data: 0.84, test data: 0.71
## Find the optimal alpha
optimal_lasso = LassoCV(n_alphas=300, cv=10, verbose=1)
optimal_lasso.fit(x_train, y_train)
print optimal_lasso.alpha_
............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
0.0282291816094
............................................................................................................[Parallel(n_jobs=1)]: Done 10 out of 10 | elapsed: 0.8s finished
## Implement the Lasso Regression
lasso = Lasso(alpha=optimal_lasso.alpha_)
## fit your regression
lasso.fit(x_train, y_train)
Lasso(alpha=0.028229181609392653, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False)
## Evaluate the Lasso Regression
rsquare_meansquare_error(y_train, y_test, x_train, x_test, "Lasso", lasso)
MSE Lasso train data: 4.50, test data: 5.17 R^2 Lasso train data: 0.85, test data: 0.72
## Find the optimal alphas
l1_ratios = np.linspace(0.01, 1.0, 50)
optimal_enet = ElasticNetCV(l1_ratio=l1_ratios, n_alphas=300, cv=5, verbose=1)
optimal_enet.fit(x_train, y_train)
print optimal_enet.alpha_
print optimal_enet.l1_ratio_
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
0.064443056188 0.070612244898
[Parallel(n_jobs=1)]: Done 250 out of 250 | elapsed: 19.6s finished
## Create a model Enet
enet = ElasticNet(alpha=optimal_enet.alpha_, l1_ratio=optimal_enet.l1_ratio_)
## Fit your model
enet.fit(x_train, y_train)
ElasticNet(alpha=0.064443056188017989, copy_X=True, fit_intercept=True,
l1_ratio=0.070612244897959184, max_iter=1000, normalize=False,
positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
## Evaluate the Elastic Net Regression
rsquare_meansquare_error(y_train, y_test, x_train, x_test, "Elastic Net", enet)
MSE Elastic Net train data: 4.60, test data: 5.27 R^2 Elastic Net train data: 0.84, test data: 0.71
''' Here I am defining a function to print the coefficients, their absolute values and the non-absolute values'''
def best_reg_method(x, best_regulari):
method_coefs = pd.DataFrame({'variable':x.columns,
'coef':best_regulari.coef_,
'abs_coef':np.abs(best_regulari.coef_)})
method_coefs.sort_values('abs_coef', inplace=True, ascending=False)
'''you can change the number inside head to display more or less variables'''
return method_coefs.head(10)
best_reg_method(x, ridge)
| abs_coef | coef | variable | |
|---|---|---|---|
| 2 | 1.740302 | 1.740302 | income_lowest% |
| 0 | 1.326838 | 1.326838 | GDP_percapita_constant |
| 7 | 1.202375 | -1.202375 | pop_growth |
| 4 | 0.729386 | -0.729386 | death_rate |
| 6 | 0.669221 | 0.669221 | trade |
| 8 | 0.668478 | 0.668478 | remittances |
| 9 | 0.584685 | -0.584685 | net_bilateral_aid |
| 1 | 0.546543 | -0.546543 | income_highest% |
| 3 | 0.307262 | 0.307262 | poverty_headcount_1.90 |
| 10 | 0.184696 | -0.184696 | FDI |
best_reg_method(x, lasso)
| abs_coef | coef | variable | |
|---|---|---|---|
| 2 | 2.253235 | 2.253235 | income_lowest% |
| 7 | 1.783755 | -1.783755 | pop_growth |
| 0 | 1.584851 | 1.584851 | GDP_percapita_constant |
| 6 | 0.989429 | 0.989429 | trade |
| 4 | 0.912235 | -0.912235 | death_rate |
| 9 | 0.696059 | -0.696059 | net_bilateral_aid |
| 3 | 0.672069 | 0.672069 | poverty_headcount_1.90 |
| 1 | 0.244444 | -0.244444 | income_highest% |
| 10 | 0.168705 | -0.168705 | FDI |
| 5 | 0.045264 | -0.045264 | unemployment |
best_reg_method(x, enet)
| abs_coef | coef | variable | |
|---|---|---|---|
| 2 | 2.014602 | 2.014602 | income_lowest% |
| 0 | 1.456899 | 1.456899 | GDP_percapita_constant |
| 7 | 1.445306 | -1.445306 | pop_growth |
| 6 | 0.810558 | 0.810558 | trade |
| 4 | 0.801342 | -0.801342 | death_rate |
| 9 | 0.650087 | -0.650087 | net_bilateral_aid |
| 3 | 0.584547 | 0.584547 | poverty_headcount_1.90 |
| 1 | 0.469064 | -0.469064 | income_highest% |
| 8 | 0.429958 | 0.429958 | remittances |
| 10 | 0.193813 | -0.193813 | FDI |
from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor()
## Here is the gridsearch
params = {"max_depth": [3,5,10,20],
"max_features": [None, "auto"],
"min_samples_leaf": [1, 3, 5, 7, 10],
"min_samples_split": [2, 5, 7],
"criterion" : ['mse']
}
# ## Here crossvalidate
from sklearn.grid_search import GridSearchCV
dtr_gs = GridSearchCV(dtr, params, n_jobs=-1, cv=5, verbose=1)
## Fit the regresion tree
dtr_gs.fit(x_train, y_train)
Fitting 5 folds for each of 120 candidates, totalling 600 fits
[Parallel(n_jobs=-1)]: Done 600 out of 600 | elapsed: 1.3s finished
GridSearchCV(cv=5, error_score='raise',
estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
max_leaf_nodes=None, min_impurity_split=1e-07,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best'),
fit_params={}, iid=True, n_jobs=-1,
param_grid={'max_features': [None, 'auto'], 'min_samples_split': [2, 5, 7], 'criterion': ['mse'], 'max_depth': [3, 5, 10, 20], 'min_samples_leaf': [1, 3, 5, 7, 10]},
pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)
## Print Best Estimator, parameters and score
''' dtr_best = is the regression tree regressor with best parameters/estimators'''
dtr_best = dtr_gs.best_estimator_
print "best estimator", dtr_best
print "\n==========\n"
print "best parameters", dtr_gs.best_params_
print "\n==========\n"
print "best score", dtr_gs.best_score_
best estimator DecisionTreeRegressor(criterion='mse', max_depth=5, max_features='auto',
max_leaf_nodes=None, min_impurity_split=1e-07,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
==========
best parameters {'max_features': 'auto', 'min_samples_split': 2, 'criterion': 'mse', 'max_depth': 5, 'min_samples_leaf': 1}
==========
best score 0.885738615734
##features that best explain your Y
''' Here I am defining a function to print feature importance using best models'''
def feature_importance(x, best_model):
feature_importance = pd.DataFrame({'feature':x.columns, 'importance':best_model.feature_importances_})
feature_importance.sort_values('importance', ascending=False, inplace=True)
return feature_importance
feature_importance(x, dtr_best)
| feature | importance | |
|---|---|---|
| 0 | GDP_percapita_constant | 8.039375e-01 |
| 8 | remittances | 1.250507e-01 |
| 9 | net_bilateral_aid | 3.330661e-02 |
| 4 | death_rate | 2.985446e-02 |
| 6 | trade | 6.407658e-03 |
| 7 | pop_growth | 1.004094e-03 |
| 10 | FDI | 3.731590e-04 |
| 3 | poverty_headcount_1.90 | 6.509575e-05 |
| 5 | unemployment | 7.798122e-07 |
| 1 | income_highest% | 0.000000e+00 |
| 2 | income_lowest% | 0.000000e+00 |
## Predict
y_pred_dtr= dtr_best.predict(x_test)
y_pred_dtr
array([ 6.97976052, 19.09690622, 6.97976052, 4.97569521,
1.34203879, 2.22139377, 4.97569521, 6.97976052,
1.34203879, 7.56872769, 1.34203879, 2.26399794,
4.97569521, 1.34203879, 6.97976052, 4.47564148,
6.97976052, 7.56872769, 4.97569521, 6.97976052,
1.34203879, 4.47564148, 4.97569521, 1.34203879,
20.94549073, 7.56872769, 4.47564148, 6.97976052,
6.97976052, 4.97569521, 4.97569521, 7.56872769, 6.97976052])
## Evaluate the Regression Tree performance on your train and test data
rsquare_meansquare_error(y_train, y_test, x_train, x_test, "Regression tree", dtr_best)
MSE Regression tree train data: 0.19, test data: 1.55 R^2 Regression tree train data: 0.99, test data: 0.92
from sklearn.ensemble import RandomForestRegressor
forest = RandomForestRegressor( )
params = {'max_depth':[3,4,5],
'max_leaf_nodes':[5,6,7],
'min_samples_split':[3,4],
'n_estimators': [100]
}
estimator_rfr = GridSearchCV(forest, params, n_jobs=-1, cv=5,verbose=1)
## Fit your random forest tree
estimator_rfr.fit(x_train, y_train)
Fitting 5 folds for each of 18 candidates, totalling 90 fits
[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 8.1s [Parallel(n_jobs=-1)]: Done 90 out of 90 | elapsed: 16.2s finished
GridSearchCV(cv=5, error_score='raise',
estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False),
fit_params={}, iid=True, n_jobs=-1,
param_grid={'min_samples_split': [3, 4], 'max_leaf_nodes': [5, 6, 7], 'n_estimators': [100], 'max_depth': [3, 4, 5]},
pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)
## Print the best estimator, parameters and score
''' rfr_best = is the random forest regression tree regressor with best parameters/estimators'''
rfr_best = estimator_rfr.best_estimator_
print "best estimator", rfr_best
print "\n==========\n"
print "best parameters", estimator_rfr.best_params_
print "\n==========\n"
print "best score", estimator_rfr.best_score_
best estimator RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,
max_features='auto', max_leaf_nodes=7, min_impurity_split=1e-07,
min_samples_leaf=1, min_samples_split=3,
min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0, warm_start=False)
==========
best parameters {'min_samples_split': 3, 'max_leaf_nodes': 7, 'n_estimators': 100, 'max_depth': 5}
==========
best score 0.879425646607
## Print the feauure importance
feature_importance(x, rfr_best)
| feature | importance | |
|---|---|---|
| 0 | GDP_percapita_constant | 0.504375 |
| 7 | pop_growth | 0.237784 |
| 8 | remittances | 0.088999 |
| 4 | death_rate | 0.076235 |
| 3 | poverty_headcount_1.90 | 0.040469 |
| 9 | net_bilateral_aid | 0.018331 |
| 2 | income_lowest% | 0.017777 |
| 1 | income_highest% | 0.008949 |
| 6 | trade | 0.003667 |
| 5 | unemployment | 0.002310 |
| 10 | FDI | 0.001104 |
## Predict
y_pred_rfdtr= rfr_best.predict(x_test)
y_pred_rfdtr
array([ 6.7452667 , 18.92962689, 6.35557839, 5.85822173,
2.26808502, 3.41398234, 5.77375152, 6.31265132,
1.49663576, 4.95788407, 1.50335546, 1.89007663,
5.26840944, 1.54260286, 6.85693493, 4.73685239,
13.14452424, 4.38669293, 6.56894847, 7.79768882,
1.82853601, 4.31161426, 5.77664529, 1.82853601,
20.33043929, 5.08040759, 3.545363 , 6.78950492,
6.74578188, 5.50926387, 5.77375152, 5.08040759, 7.47567285])
## Evaluate your model
rsquare_meansquare_error(y_train, y_test, x_train, x_test, "Random Forest Regression tree", rfr_best)
MSE Random Forest Regression tree train data: 0.84, test data: 1.45 R^2 Random Forest Regression tree train data: 0.97, test data: 0.92
print migration_flows['Migration'].mean()
5.86008409129
migration_flows['threshold'] = migration_flows['Migration'] >= 5.86008409129
migration_flows['threshold']= migration_flows['threshold'].apply(lambda x: 1 if x== True else 0)
migration_flows
| Year | Country | Migration | GDP_percapita_constant | income_highest% | income_lowest% | poverty_headcount_1.90 | death_rate | unemployment | trade | pop_growth | remittances | net_bilateral_aid | FDI | threshold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970-01-01 00:00:00.000001980 | El Salvador | 2.063205 | 2572.813235 | 40.809231 | 1.104423 | 18.977037 | 11.681 | 13.340000 | 67.406464 | 1.739184 | 1.372147 | 43000000 | 0.014116 | 0 |
| 1 | 1970-01-01 00:00:00.000001980 | Guatemala | 0.886027 | 2560.782037 | 40.809231 | 1.104423 | 18.977037 | 11.568 | 5.496944 | 47.105487 | 2.635143 | 0.332542 | 17000000 | 0.025385 | 0 |
| 2 | 1970-01-01 00:00:00.000001980 | Honduras | 1.076884 | 1655.946421 | 40.809231 | 1.104423 | 18.977037 | 10.233 | 5.496944 | 81.293839 | 3.145300 | 0.062354 | 19000000 | 0.038971 | 0 |
| 3 | 1970-01-01 00:00:00.000001981 | El Salvador | 1.342039 | 2267.095959 | 40.809231 | 1.104423 | 18.977037 | 11.494 | 5.496944 | 60.266492 | 1.611673 | 2.108693 | 97000000 | 0.014116 | 0 |
| 4 | 1970-01-01 00:00:00.000001981 | Guatemala | 1.342039 | 2509.736778 | 40.809231 | 1.104423 | 18.977037 | 11.300 | 2.150000 | 40.691257 | 2.658257 | 0.284635 | 18000000 | -0.011618 | 0 |
| 5 | 1970-01-01 00:00:00.000001981 | Honduras | 1.342039 | 1645.846419 | 40.809231 | 1.104423 | 18.977037 | 9.793 | 5.496944 | 69.338535 | 3.113439 | 0.062068 | 35000000 | 0.070935 | 0 |
| 6 | 1970-01-01 00:00:00.000001982 | El Salvador | 1.342039 | 2092.554425 | 40.809231 | 1.104423 | 18.977037 | 11.251 | 5.496944 | 51.247740 | 1.498034 | 3.300787 | 170000000 | 0.014116 | 0 |
| 7 | 1970-01-01 00:00:00.000001982 | Guatemala | 1.342039 | 2357.368296 | 40.809231 | 1.104423 | 18.977037 | 11.016 | 2.270000 | 33.474818 | 2.669298 | 0.122749 | 20000000 | -0.045887 | 0 |
| 8 | 1970-01-01 00:00:00.000001982 | Honduras | 1.342039 | 1573.671559 | 40.809231 | 1.104423 | 18.977037 | 9.359 | 7.300000 | 54.727051 | 3.083349 | 0.051662 | 68000000 | -0.034441 | 0 |
| 9 | 1970-01-01 00:00:00.000001983 | El Salvador | 1.342039 | 2094.864582 | 40.809231 | 1.104423 | 18.977037 | 10.953 | 5.496944 | 54.397801 | 1.413014 | 3.292315 | 231000000 | 0.014116 | 0 |
| 10 | 1970-01-01 00:00:00.000001983 | Guatemala | 1.342039 | 2236.567544 | 40.809231 | 1.104423 | 18.977037 | 10.718 | 5.496944 | 27.546959 | 2.654604 | 0.043094 | 36000000 | 0.000000 | 0 |
| 11 | 1970-01-01 00:00:00.000001983 | Honduras | 1.342039 | 1512.185833 | 40.809231 | 1.104423 | 18.977037 | 8.933 | 5.496944 | 55.394868 | 3.056878 | 0.058499 | 64000000 | 0.064998 | 0 |
| 12 | 1970-01-01 00:00:00.000001984 | El Salvador | 1.342039 | 2094.098791 | 40.809231 | 1.104423 | 18.977037 | 10.602 | 5.496944 | 50.290675 | 1.364660 | 4.345542 | 221000000 | 0.014116 | 0 |
| 13 | 1970-01-01 00:00:00.000001984 | Guatemala | 1.342039 | 2189.829730 | 40.809231 | 1.104423 | 18.977037 | 10.406 | 5.496944 | 28.153115 | 2.607323 | 0.035903 | 29000000 | 0.052798 | 0 |
| 14 | 1970-01-01 00:00:00.000001984 | Honduras | 1.342039 | 1530.695403 | 40.809231 | 1.104423 | 18.977037 | 8.520 | 5.496944 | 57.728231 | 3.037319 | 0.058753 | 123000000 | -0.030130 | 0 |
| 15 | 1970-01-01 00:00:00.000001985 | El Salvador | 1.342039 | 2078.900486 | 40.809231 | 1.104423 | 18.977037 | 10.205 | 16.950001 | 52.210538 | 1.343117 | 4.135388 | 287000000 | 0.014116 | 0 |
| 16 | 1970-01-01 00:00:00.000001985 | Guatemala | 1.342039 | 2121.873660 | 40.809231 | 1.104423 | 18.977037 | 10.085 | 5.496944 | 24.932246 | 2.541226 | 0.010286 | 50000000 | 0.000000 | 0 |
| 17 | 1970-01-01 00:00:00.000001985 | Honduras | 1.342039 | 1547.357836 | 40.809231 | 1.104423 | 18.977037 | 8.129 | 5.496944 | 54.966344 | 3.020231 | 0.057700 | 161000000 | 0.000000 | 0 |
| 18 | 1970-01-01 00:00:00.000001986 | El Salvador | 1.342039 | 2055.438830 | 40.809231 | 1.104423 | 18.977037 | 9.775 | 7.900000 | 53.714123 | 1.324193 | 4.170338 | 272000000 | 0.014116 | 0 |
| 19 | 1970-01-01 00:00:00.000001986 | Guatemala | 1.342039 | 2073.066614 | 46.730000 | 1.000000 | 50.940000 | 9.762 | 5.496944 | 30.644019 | 2.470001 | 0.009679 | 86000000 | 0.000000 | 0 |
| 20 | 1970-01-01 00:00:00.000001986 | Honduras | 1.342039 | 1512.507552 | 43.260000 | 1.230000 | 25.280000 | 7.769 | 12.120000 | 54.890376 | 2.998073 | 0.055140 | 175000000 | 0.000000 | 0 |
| 21 | 1970-01-01 00:00:00.000001987 | El Salvador | 1.342039 | 2079.844180 | 40.809231 | 1.104423 | 18.977037 | 9.330 | 5.496944 | 45.094622 | 1.302156 | 4.715458 | 356000000 | 0.014116 | 0 |
| 22 | 1970-01-01 00:00:00.000001987 | Guatemala | 1.342039 | 2095.342199 | 40.809231 | 1.104423 | 18.977037 | 9.444 | 3.500000 | 38.142963 | 2.413838 | 0.001412 | 155000000 | 0.014116 | 0 |
| 23 | 1970-01-01 00:00:00.000001987 | Honduras | 1.342039 | 1556.855276 | 40.809231 | 1.104423 | 18.977037 | 7.447 | 11.400000 | 48.789886 | 2.966596 | 0.811559 | 153000000 | 0.024082 | 0 |
| 24 | 1970-01-01 00:00:00.000001988 | El Salvador | 1.342039 | 2091.693100 | 40.809231 | 1.104423 | 18.977037 | 8.893 | 9.370000 | 38.095704 | 1.291964 | 5.029738 | 318000000 | 0.014116 | 0 |
| 25 | 1970-01-01 00:00:00.000001988 | Guatemala | 1.342039 | 2125.624163 | 40.809231 | 1.104423 | 18.977037 | 9.137 | 5.496944 | 38.039914 | 2.383684 | 0.582789 | 134000000 | -0.012752 | 0 |
| 26 | 1970-01-01 00:00:00.000001988 | Honduras | 1.342039 | 1581.639092 | 40.809231 | 1.104423 | 18.977037 | 7.164 | 5.496944 | 55.215652 | 2.927378 | 1.052794 | 155000000 | -0.025186 | 0 |
| 27 | 1970-01-01 00:00:00.000001989 | El Salvador | 1.342039 | 2084.671422 | 40.809231 | 1.104423 | 18.980000 | 8.480 | 8.350000 | 36.928296 | 1.293850 | 5.439348 | 310000000 | 0.014116 | 0 |
| 28 | 1970-01-01 00:00:00.000001989 | Guatemala | 1.342039 | 2157.313890 | 46.780000 | 0.680000 | 38.020000 | 8.845 | 2.000000 | 39.781546 | 2.387014 | 1.010615 | 146000000 | -0.047558 | 0 |
| 29 | 1970-01-01 00:00:00.000001989 | Honduras | 1.342039 | 1603.219717 | 48.180000 | 1.040000 | 38.600000 | 6.920 | 5.496944 | 65.347396 | 2.879679 | 1.363847 | 102000000 | -0.028063 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 1970-01-01 00:00:00.000002006 | El Salvador | 17.546949 | 3475.866745 | 35.480000 | 1.790000 | 6.360000 | 6.672 | 6.570000 | 71.849041 | 0.341593 | 18.773955 | 24540000 | -0.141774 | 1 |
| 79 | 1970-01-01 00:00:00.000002006 | Guatemala | 5.343950 | 2698.985240 | 43.560000 | 1.070000 | 11.510000 | 5.663 | 1.820000 | 66.818187 | 2.298528 | 12.239366 | 67250000 | 0.276205 | 0 |
| 80 | 1970-01-01 00:00:00.000002006 | Honduras | 5.783592 | 2017.943010 | 44.050000 | 0.580000 | 23.790000 | 5.105 | 3.110000 | 133.131835 | 1.826883 | 21.557383 | 84100000 | 0.452738 | 0 |
| 81 | 1970-01-01 00:00:00.000002007 | El Salvador | 18.448273 | 3597.961991 | 35.720000 | 1.930000 | 4.490000 | 6.662 | 6.410000 | 74.177439 | 0.315511 | 18.448488 | 39040000 | 0.473516 | 1 |
| 82 | 1970-01-01 00:00:00.000002007 | Guatemala | 5.077445 | 2805.169791 | 40.809231 | 1.104423 | 18.977037 | 5.616 | 5.496944 | 67.898497 | 2.254506 | 12.418103 | 45710000 | 0.408934 | 0 |
| 83 | 1970-01-01 00:00:00.000002007 | Honduras | 6.034761 | 2104.759589 | 43.810000 | 0.900000 | 17.430000 | 5.077 | 2.920000 | 135.070635 | 1.792143 | 21.291564 | 71100000 | 0.332742 | 1 |
| 84 | 1970-01-01 00:00:00.000002008 | El Salvador | 18.237120 | 3633.014903 | 36.040000 | 1.700000 | 6.920000 | 6.659 | 5.880000 | 76.580188 | 0.296649 | 17.520181 | 42370000 | 0.370631 | 1 |
| 85 | 1970-01-01 00:00:00.000002008 | Guatemala | 5.240451 | 2833.735795 | 40.809231 | 1.104423 | 18.977037 | 5.576 | 5.496944 | 64.125228 | 2.215217 | 11.395396 | 70350000 | 0.034880 | 0 |
| 86 | 1970-01-01 00:00:00.000002008 | Honduras | 6.339264 | 2155.827865 | 43.870000 | 0.910000 | 16.140000 | 5.055 | 2.990000 | 135.748955 | 1.747160 | 20.459753 | 96330000 | 1.402889 | 1 |
| 87 | 1970-01-01 00:00:00.000002009 | El Salvador | 19.096906 | 3509.156436 | 36.070000 | 1.780000 | 6.390000 | 6.663 | 7.330000 | 61.871642 | 0.285542 | 16.467451 | 82080000 | 0.014327 | 1 |
| 88 | 1970-01-01 00:00:00.000002009 | Guatemala | 5.539466 | 2787.128287 | 40.809231 | 1.104423 | 18.977037 | 5.539 | 5.496944 | 57.105993 | 2.183077 | 10.651859 | 83890000 | 0.325371 | 0 |
| 89 | 1970-01-01 00:00:00.000002009 | Honduras | 6.338030 | 2068.185180 | 39.140000 | 1.150000 | 14.040000 | 5.038 | 3.280000 | 96.905006 | 1.688651 | 17.101447 | 128760000 | -0.074317 | 1 |
| 90 | 1970-01-01 00:00:00.000002010 | El Salvador | 20.105788 | 3547.070983 | 33.700000 | 1.670000 | 7.240000 | 6.673 | 7.050000 | 68.768763 | 0.280903 | 16.209675 | 148160000 | 0.524807 | 1 |
| 91 | 1970-01-01 00:00:00.000002010 | Guatemala | 5.639487 | 2805.951416 | 40.809231 | 1.104423 | 18.977037 | 5.503 | 3.740000 | 62.114932 | 2.156000 | 10.236867 | 100500000 | 0.153036 | 0 |
| 92 | 1970-01-01 00:00:00.000002010 | Honduras | 6.964149 | 2110.822021 | 41.020000 | 1.090000 | 15.470000 | 5.026 | 4.100000 | 109.441838 | 1.622622 | 16.643140 | 100840000 | -2.309012 | 1 |
| 93 | 1970-01-01 00:00:00.000002011 | El Salvador | 20.886863 | 3615.583230 | 32.860000 | 2.110000 | 4.530000 | 6.692 | 6.620000 | 74.643243 | 0.279522 | 15.748084 | 162440000 | -0.414578 | 1 |
| 94 | 1970-01-01 00:00:00.000002011 | Guatemala | 5.653971 | 2861.167894 | 41.830000 | 1.340000 | 11.530000 | 5.467 | 4.130000 | 63.984196 | 2.129043 | 9.492785 | 93080000 | 0.274376 | 0 |
| 95 | 1970-01-01 00:00:00.000002011 | Honduras | 6.437598 | 2157.984444 | 45.670000 | 0.750000 | 18.750000 | 5.017 | 4.270000 | 122.216903 | 1.554236 | 15.980083 | 46360000 | 0.172124 | 1 |
| 96 | 1970-01-01 00:00:00.000002012 | El Salvador | 20.945491 | 3673.262887 | 32.470000 | 2.150000 | 4.160000 | 6.718 | 6.070000 | 69.698828 | 0.280768 | 16.361001 | 150850000 | -0.150729 | 1 |
| 97 | 1970-01-01 00:00:00.000002012 | Guatemala | 5.586203 | 2884.897429 | 40.809231 | 1.104423 | 18.977037 | 5.433 | 2.870000 | 60.982475 | 2.100666 | 9.983742 | 95490000 | 0.115398 | 0 |
| 98 | 1970-01-01 00:00:00.000002012 | Honduras | 6.743448 | 2213.759527 | 45.680000 | 0.790000 | 21.360000 | 5.012 | 5.496944 | 121.188216 | 1.493978 | 15.871110 | 52650000 | 1.177095 | 1 |
| 99 | 1970-01-01 00:00:00.000002013 | El Salvador | 20.560594 | 3730.422292 | 34.350000 | 2.110000 | 3.250000 | 6.751 | 5.930000 | 71.948881 | 0.286321 | 16.241404 | 51090000 | 0.271335 | 1 |
| 100 | 1970-01-01 00:00:00.000002013 | Guatemala | 5.750461 | 2930.170750 | 40.809231 | 1.104423 | 18.977037 | 5.401 | 2.990000 | 58.548341 | 2.073729 | 9.988782 | 102670000 | 0.169948 | 0 |
| 101 | 1970-01-01 00:00:00.000002013 | Honduras | 6.798242 | 2242.818455 | 41.480000 | 0.980000 | 18.930000 | 5.010 | 3.910000 | 116.306049 | 1.449196 | 16.863760 | 90910000 | 0.421417 | 1 |
| 102 | 1970-01-01 00:00:00.000002014 | El Salvador | 21.537939 | 3772.401570 | 32.310000 | 2.190000 | 2.970000 | 6.790 | 5.496944 | 69.570771 | 0.296163 | 16.567886 | 45370000 | 0.791026 | 1 |
| 103 | 1970-01-01 00:00:00.000002014 | Guatemala | 5.716933 | 2990.594485 | 38.360000 | 1.640000 | 9.320000 | 5.370 | 2.910000 | 56.717915 | 2.048252 | 9.941701 | 126040000 | -0.198735 | 0 |
| 104 | 1970-01-01 00:00:00.000002014 | Honduras | 7.389157 | 2279.309902 | 38.360000 | 1.150000 | 15.960000 | 5.011 | 5.496944 | 112.609235 | 1.424638 | 17.385695 | 80450000 | 0.898223 | 1 |
| 105 | 1970-01-01 00:00:00.000002015 | El Salvador | 22.073593 | 3853.107631 | 40.809231 | 1.104423 | 18.977037 | 6.833 | 5.496944 | 67.989029 | 0.308592 | 16.577147 | 47470000 | 0.346951 | 1 |
| 106 | 1970-01-01 00:00:00.000002015 | Guatemala | 5.675817 | 3052.270569 | 40.809231 | 1.104423 | 18.977037 | 5.339 | 2.420000 | 51.333403 | 2.023674 | 10.303115 | 123500000 | 0.049519 | 0 |
| 107 | 1970-01-01 00:00:00.000002015 | Honduras | 7.418273 | 2329.002149 | 40.809231 | 1.104423 | 18.977037 | 5.015 | 7.380000 | 107.434916 | 1.414027 | 17.953123 | 110380000 | 0.998200 | 1 |
108 rows × 15 columns
migration_flows.threshold.value_counts()
0 72 1 36 Name: threshold, dtype: int64
migration_cont = migration_flows.iloc[:, 3:-1]
migration_cont
| GDP_percapita_constant | income_highest% | income_lowest% | poverty_headcount_1.90 | death_rate | unemployment | trade | pop_growth | remittances | net_bilateral_aid | FDI | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2572.813235 | 40.809231 | 1.104423 | 18.977037 | 11.681 | 13.340000 | 67.406464 | 1.739184 | 1.372147 | 43000000 | 0.014116 |
| 1 | 2560.782037 | 40.809231 | 1.104423 | 18.977037 | 11.568 | 5.496944 | 47.105487 | 2.635143 | 0.332542 | 17000000 | 0.025385 |
| 2 | 1655.946421 | 40.809231 | 1.104423 | 18.977037 | 10.233 | 5.496944 | 81.293839 | 3.145300 | 0.062354 | 19000000 | 0.038971 |
| 3 | 2267.095959 | 40.809231 | 1.104423 | 18.977037 | 11.494 | 5.496944 | 60.266492 | 1.611673 | 2.108693 | 97000000 | 0.014116 |
| 4 | 2509.736778 | 40.809231 | 1.104423 | 18.977037 | 11.300 | 2.150000 | 40.691257 | 2.658257 | 0.284635 | 18000000 | -0.011618 |
| 5 | 1645.846419 | 40.809231 | 1.104423 | 18.977037 | 9.793 | 5.496944 | 69.338535 | 3.113439 | 0.062068 | 35000000 | 0.070935 |
| 6 | 2092.554425 | 40.809231 | 1.104423 | 18.977037 | 11.251 | 5.496944 | 51.247740 | 1.498034 | 3.300787 | 170000000 | 0.014116 |
| 7 | 2357.368296 | 40.809231 | 1.104423 | 18.977037 | 11.016 | 2.270000 | 33.474818 | 2.669298 | 0.122749 | 20000000 | -0.045887 |
| 8 | 1573.671559 | 40.809231 | 1.104423 | 18.977037 | 9.359 | 7.300000 | 54.727051 | 3.083349 | 0.051662 | 68000000 | -0.034441 |
| 9 | 2094.864582 | 40.809231 | 1.104423 | 18.977037 | 10.953 | 5.496944 | 54.397801 | 1.413014 | 3.292315 | 231000000 | 0.014116 |
| 10 | 2236.567544 | 40.809231 | 1.104423 | 18.977037 | 10.718 | 5.496944 | 27.546959 | 2.654604 | 0.043094 | 36000000 | 0.000000 |
| 11 | 1512.185833 | 40.809231 | 1.104423 | 18.977037 | 8.933 | 5.496944 | 55.394868 | 3.056878 | 0.058499 | 64000000 | 0.064998 |
| 12 | 2094.098791 | 40.809231 | 1.104423 | 18.977037 | 10.602 | 5.496944 | 50.290675 | 1.364660 | 4.345542 | 221000000 | 0.014116 |
| 13 | 2189.829730 | 40.809231 | 1.104423 | 18.977037 | 10.406 | 5.496944 | 28.153115 | 2.607323 | 0.035903 | 29000000 | 0.052798 |
| 14 | 1530.695403 | 40.809231 | 1.104423 | 18.977037 | 8.520 | 5.496944 | 57.728231 | 3.037319 | 0.058753 | 123000000 | -0.030130 |
| 15 | 2078.900486 | 40.809231 | 1.104423 | 18.977037 | 10.205 | 16.950001 | 52.210538 | 1.343117 | 4.135388 | 287000000 | 0.014116 |
| 16 | 2121.873660 | 40.809231 | 1.104423 | 18.977037 | 10.085 | 5.496944 | 24.932246 | 2.541226 | 0.010286 | 50000000 | 0.000000 |
| 17 | 1547.357836 | 40.809231 | 1.104423 | 18.977037 | 8.129 | 5.496944 | 54.966344 | 3.020231 | 0.057700 | 161000000 | 0.000000 |
| 18 | 2055.438830 | 40.809231 | 1.104423 | 18.977037 | 9.775 | 7.900000 | 53.714123 | 1.324193 | 4.170338 | 272000000 | 0.014116 |
| 19 | 2073.066614 | 46.730000 | 1.000000 | 50.940000 | 9.762 | 5.496944 | 30.644019 | 2.470001 | 0.009679 | 86000000 | 0.000000 |
| 20 | 1512.507552 | 43.260000 | 1.230000 | 25.280000 | 7.769 | 12.120000 | 54.890376 | 2.998073 | 0.055140 | 175000000 | 0.000000 |
| 21 | 2079.844180 | 40.809231 | 1.104423 | 18.977037 | 9.330 | 5.496944 | 45.094622 | 1.302156 | 4.715458 | 356000000 | 0.014116 |
| 22 | 2095.342199 | 40.809231 | 1.104423 | 18.977037 | 9.444 | 3.500000 | 38.142963 | 2.413838 | 0.001412 | 155000000 | 0.014116 |
| 23 | 1556.855276 | 40.809231 | 1.104423 | 18.977037 | 7.447 | 11.400000 | 48.789886 | 2.966596 | 0.811559 | 153000000 | 0.024082 |
| 24 | 2091.693100 | 40.809231 | 1.104423 | 18.977037 | 8.893 | 9.370000 | 38.095704 | 1.291964 | 5.029738 | 318000000 | 0.014116 |
| 25 | 2125.624163 | 40.809231 | 1.104423 | 18.977037 | 9.137 | 5.496944 | 38.039914 | 2.383684 | 0.582789 | 134000000 | -0.012752 |
| 26 | 1581.639092 | 40.809231 | 1.104423 | 18.977037 | 7.164 | 5.496944 | 55.215652 | 2.927378 | 1.052794 | 155000000 | -0.025186 |
| 27 | 2084.671422 | 40.809231 | 1.104423 | 18.980000 | 8.480 | 8.350000 | 36.928296 | 1.293850 | 5.439348 | 310000000 | 0.014116 |
| 28 | 2157.313890 | 46.780000 | 0.680000 | 38.020000 | 8.845 | 2.000000 | 39.781546 | 2.387014 | 1.010615 | 146000000 | -0.047558 |
| 29 | 1603.219717 | 48.180000 | 1.040000 | 38.600000 | 6.920 | 5.496944 | 65.347396 | 2.879679 | 1.363847 | 102000000 | -0.028063 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 3475.866745 | 35.480000 | 1.790000 | 6.360000 | 6.672 | 6.570000 | 71.849041 | 0.341593 | 18.773955 | 24540000 | -0.141774 |
| 79 | 2698.985240 | 43.560000 | 1.070000 | 11.510000 | 5.663 | 1.820000 | 66.818187 | 2.298528 | 12.239366 | 67250000 | 0.276205 |
| 80 | 2017.943010 | 44.050000 | 0.580000 | 23.790000 | 5.105 | 3.110000 | 133.131835 | 1.826883 | 21.557383 | 84100000 | 0.452738 |
| 81 | 3597.961991 | 35.720000 | 1.930000 | 4.490000 | 6.662 | 6.410000 | 74.177439 | 0.315511 | 18.448488 | 39040000 | 0.473516 |
| 82 | 2805.169791 | 40.809231 | 1.104423 | 18.977037 | 5.616 | 5.496944 | 67.898497 | 2.254506 | 12.418103 | 45710000 | 0.408934 |
| 83 | 2104.759589 | 43.810000 | 0.900000 | 17.430000 | 5.077 | 2.920000 | 135.070635 | 1.792143 | 21.291564 | 71100000 | 0.332742 |
| 84 | 3633.014903 | 36.040000 | 1.700000 | 6.920000 | 6.659 | 5.880000 | 76.580188 | 0.296649 | 17.520181 | 42370000 | 0.370631 |
| 85 | 2833.735795 | 40.809231 | 1.104423 | 18.977037 | 5.576 | 5.496944 | 64.125228 | 2.215217 | 11.395396 | 70350000 | 0.034880 |
| 86 | 2155.827865 | 43.870000 | 0.910000 | 16.140000 | 5.055 | 2.990000 | 135.748955 | 1.747160 | 20.459753 | 96330000 | 1.402889 |
| 87 | 3509.156436 | 36.070000 | 1.780000 | 6.390000 | 6.663 | 7.330000 | 61.871642 | 0.285542 | 16.467451 | 82080000 | 0.014327 |
| 88 | 2787.128287 | 40.809231 | 1.104423 | 18.977037 | 5.539 | 5.496944 | 57.105993 | 2.183077 | 10.651859 | 83890000 | 0.325371 |
| 89 | 2068.185180 | 39.140000 | 1.150000 | 14.040000 | 5.038 | 3.280000 | 96.905006 | 1.688651 | 17.101447 | 128760000 | -0.074317 |
| 90 | 3547.070983 | 33.700000 | 1.670000 | 7.240000 | 6.673 | 7.050000 | 68.768763 | 0.280903 | 16.209675 | 148160000 | 0.524807 |
| 91 | 2805.951416 | 40.809231 | 1.104423 | 18.977037 | 5.503 | 3.740000 | 62.114932 | 2.156000 | 10.236867 | 100500000 | 0.153036 |
| 92 | 2110.822021 | 41.020000 | 1.090000 | 15.470000 | 5.026 | 4.100000 | 109.441838 | 1.622622 | 16.643140 | 100840000 | -2.309012 |
| 93 | 3615.583230 | 32.860000 | 2.110000 | 4.530000 | 6.692 | 6.620000 | 74.643243 | 0.279522 | 15.748084 | 162440000 | -0.414578 |
| 94 | 2861.167894 | 41.830000 | 1.340000 | 11.530000 | 5.467 | 4.130000 | 63.984196 | 2.129043 | 9.492785 | 93080000 | 0.274376 |
| 95 | 2157.984444 | 45.670000 | 0.750000 | 18.750000 | 5.017 | 4.270000 | 122.216903 | 1.554236 | 15.980083 | 46360000 | 0.172124 |
| 96 | 3673.262887 | 32.470000 | 2.150000 | 4.160000 | 6.718 | 6.070000 | 69.698828 | 0.280768 | 16.361001 | 150850000 | -0.150729 |
| 97 | 2884.897429 | 40.809231 | 1.104423 | 18.977037 | 5.433 | 2.870000 | 60.982475 | 2.100666 | 9.983742 | 95490000 | 0.115398 |
| 98 | 2213.759527 | 45.680000 | 0.790000 | 21.360000 | 5.012 | 5.496944 | 121.188216 | 1.493978 | 15.871110 | 52650000 | 1.177095 |
| 99 | 3730.422292 | 34.350000 | 2.110000 | 3.250000 | 6.751 | 5.930000 | 71.948881 | 0.286321 | 16.241404 | 51090000 | 0.271335 |
| 100 | 2930.170750 | 40.809231 | 1.104423 | 18.977037 | 5.401 | 2.990000 | 58.548341 | 2.073729 | 9.988782 | 102670000 | 0.169948 |
| 101 | 2242.818455 | 41.480000 | 0.980000 | 18.930000 | 5.010 | 3.910000 | 116.306049 | 1.449196 | 16.863760 | 90910000 | 0.421417 |
| 102 | 3772.401570 | 32.310000 | 2.190000 | 2.970000 | 6.790 | 5.496944 | 69.570771 | 0.296163 | 16.567886 | 45370000 | 0.791026 |
| 103 | 2990.594485 | 38.360000 | 1.640000 | 9.320000 | 5.370 | 2.910000 | 56.717915 | 2.048252 | 9.941701 | 126040000 | -0.198735 |
| 104 | 2279.309902 | 38.360000 | 1.150000 | 15.960000 | 5.011 | 5.496944 | 112.609235 | 1.424638 | 17.385695 | 80450000 | 0.898223 |
| 105 | 3853.107631 | 40.809231 | 1.104423 | 18.977037 | 6.833 | 5.496944 | 67.989029 | 0.308592 | 16.577147 | 47470000 | 0.346951 |
| 106 | 3052.270569 | 40.809231 | 1.104423 | 18.977037 | 5.339 | 2.420000 | 51.333403 | 2.023674 | 10.303115 | 123500000 | 0.049519 |
| 107 | 2329.002149 | 40.809231 | 1.104423 | 18.977037 | 5.015 | 7.380000 | 107.434916 | 1.414027 | 17.953123 | 110380000 | 0.998200 |
108 rows × 11 columns
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
migration_cont_n = ss.fit_transform(migration_cont)
migration_cont_n
array([[ 3.09990924e-01, 2.49079191e-15, -6.78250390e-16, ...,
-1.03044645e+00, -7.97539512e-01, 1.39632884e-17],
[ 2.90287283e-01, 2.49079191e-15, -6.78250390e-16, ...,
-1.19160228e+00, -1.16667096e+00, 9.07069956e-03],
[ -1.19157316e+00, 2.49079191e-15, -6.78250390e-16, ...,
-1.23348589e+00, -1.13827623e+00, 2.00066728e-02],
...,
[ 2.40674484e+00, 2.49079191e-15, -6.78250390e-16, ...,
1.32657722e+00, -7.34077298e-01, 2.67908201e-01],
[ 1.09520411e+00, 2.49079191e-15, -6.78250390e-16, ...,
3.53999624e-01, 3.45348233e-01, 2.84966462e-02],
[ -8.93014977e-02, 2.49079191e-15, -6.78250390e-16, ...,
1.53987595e+00, 1.59078827e-01, 7.92117484e-01]])
## Fit the PCA and print the components
from sklearn.decomposition import PCA
migration_pca = PCA().fit(migration_cont_n)
print "Number of PCA components is: \n", migration_pca.n_components_
print "\n======\n"
print "List of PCA components is:\n", migration_pca.components_
Number of PCA components is:
11
======
List of PCA components is:
[[ 4.44953785e-01 -4.33783948e-01 3.34104401e-01 -4.24088228e-01
-1.06912171e-01 9.48163627e-02 1.37408511e-02 -4.19167628e-01
3.51743673e-01 -3.09566472e-02 3.71274547e-02]
[ -1.21666677e-02 2.08096986e-01 -2.33634354e-01 7.65655577e-02
-5.38365104e-01 -3.05100519e-01 5.50558972e-01 -5.63939732e-02
3.96597653e-01 -2.22691862e-01 -4.28025433e-04]
[ -1.48957745e-01 1.06678958e-01 -2.21507866e-01 1.50903978e-01
-6.02258276e-03 5.57226489e-01 1.98828498e-01 -3.10661936e-01
2.04701578e-01 5.77502700e-01 2.69444055e-01]
[ -3.77536854e-02 1.75339147e-02 2.85664035e-02 -2.71247993e-02
-6.39828471e-02 -3.73895302e-02 4.62671591e-02 -7.30312143e-02
4.20089511e-02 3.94499973e-01 -9.09040404e-01]
[ 3.48318407e-01 1.49689192e-01 -5.76707332e-01 1.23864812e-01
2.74527548e-01 2.22756631e-01 -1.79594534e-01 -3.23647293e-01
7.19247322e-02 -4.33903578e-01 -2.30000987e-01]
[ -3.00602698e-01 -1.04249633e-01 1.84363842e-01 -1.63764722e-01
1.71659942e-02 6.24551610e-01 3.94588512e-01 1.95176281e-01
-8.54131778e-02 -4.53507556e-01 -2.02097371e-01]
[ 2.80858239e-01 1.43466519e-01 3.58370256e-01 5.50577572e-01
-5.02721107e-01 3.09970041e-01 -3.22754836e-01 6.82383505e-02
1.50828276e-04 -9.21459990e-02 -5.33207368e-02]
[ 1.64146336e-02 8.28126884e-02 4.49229254e-01 4.96082911e-01
5.24508557e-01 -1.89871602e-01 3.63412439e-01 -2.90460448e-01
9.75618391e-02 -8.63290902e-02 -2.00023246e-02]
[ 2.08960689e-01 7.59519081e-01 2.40702424e-01 -3.90100905e-01
1.83567171e-01 9.13798292e-02 -4.82576106e-02 2.20294840e-01
2.70961964e-01 5.01216013e-02 2.26156889e-02]
[ -6.17462634e-02 3.36679898e-01 1.24097352e-01 -2.14175080e-01
-2.36145089e-01 -4.73309909e-02 7.86232942e-02 -5.80332938e-01
-6.45080067e-01 -5.76723427e-02 3.37026563e-02]
[ 6.63278746e-01 -7.68197951e-02 -1.17269424e-01 3.65260156e-02
4.05405163e-02 6.01316506e-02 4.71506725e-01 3.19519314e-01
-4.04701878e-01 2.11156656e-01 3.21322350e-02]]
## transform => Apply dimensionality reduction to X.
migration_pcs = migration_pca.transform(migration_cont_n)
migration_pcs
array([[-0.09475011, -2.55902429, 1.13077881, ..., 0.46564207,
0.03408878, 0.69305284],
[-0.91575677, -1.98239954, -1.4239706 , ..., 0.36246894,
-0.35480985, 0.45654761],
[-1.75284795, -0.93167753, -1.1262195 , ..., -0.01598467,
-0.32290655, 0.26322268],
...,
[ 2.36805819, 0.86440093, 0.13611338, ..., 0.38771311,
0.16832743, 0.30748464],
[ 0.49046578, 0.62675761, -0.78621404, ..., 0.11704958,
-0.18836398, 0.30903495],
[ 0.96671037, 1.8038798 , 1.53175003, ..., 0.09377444,
-0.31034041, -0.10472607]])
## Now create the dataframe
migration_pcs = pd.DataFrame(migration_pcs, columns=['PC'+str(i) for i in range(1, migration_pcs.shape[1]+1)])
migration_pcs['threshold'] = migration_flows.threshold
migration_pcs
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | PC11 | threshold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.094750 | -2.559024 | 1.130779 | -0.640239 | 1.828362 | 2.409852 | -0.045526 | 0.654374 | 0.465642 | 0.034089 | 0.693053 | 0 |
| 1 | -0.915757 | -1.982400 | -1.423971 | -0.787201 | 1.006300 | 0.456770 | -0.686899 | 0.673341 | 0.362469 | -0.354810 | 0.456548 | 0 |
| 2 | -1.752848 | -0.931678 | -1.126220 | -0.671409 | -0.153935 | 1.499758 | -1.110063 | 0.545378 | -0.015985 | -0.322907 | 0.263223 | 0 |
| 3 | -0.540747 | -1.759718 | -0.162042 | -0.185934 | 0.667831 | 0.016410 | -1.147953 | 1.109195 | 0.090771 | 0.194729 | 0.100909 | 0 |
| 4 | -1.089305 | -1.614602 | -2.248583 | -0.703059 | 0.659168 | -0.483891 | -0.992225 | 0.765459 | 0.205943 | -0.280907 | 0.212460 | 0 |
| 5 | -1.730771 | -1.095974 | -1.060670 | -0.607031 | -0.237551 | 1.209526 | -0.876000 | 0.249212 | -0.038364 | -0.290328 | 0.069523 | 0 |
| 6 | -0.568150 | -2.019582 | 0.493532 | 0.244410 | 0.200131 | -0.544758 | -1.158457 | 0.881710 | 0.094860 | 0.117630 | -0.151819 | 0 |
| 7 | -1.199071 | -1.708567 | -2.236150 | -0.663807 | 0.577186 | -0.488600 | -0.882825 | 0.565188 | 0.139945 | -0.246635 | -0.063284 | 0 |
| 8 | -1.697105 | -1.599834 | -0.470535 | -0.376321 | -0.249953 | 1.295664 | -0.422549 | -0.245478 | 0.001974 | -0.313593 | -0.143937 | 0 |
| 9 | -0.531903 | -2.055599 | 1.048910 | 0.609119 | -0.206761 | -0.915087 | -1.200603 | 0.794274 | 0.080778 | 0.175153 | 0.050038 | 0 |
| 10 | -1.147356 | -2.206898 | -1.352877 | -0.649862 | 0.702034 | 0.211710 | -0.391360 | 0.124095 | 0.207912 | -0.271063 | -0.175285 | 0 |
| 11 | -1.769911 | -1.215291 | -0.870798 | -0.421142 | -0.504485 | 0.863736 | -0.577620 | -0.203546 | -0.139473 | -0.191269 | -0.273811 | 0 |
| 12 | -0.427941 | -1.937353 | 0.989404 | 0.569404 | -0.139926 | -0.939164 | -1.046679 | 0.683846 | 0.077178 | 0.144913 | -0.145186 | 0 |
| 13 | -1.134854 | -2.077429 | -1.364454 | -0.708813 | 0.676491 | 0.266166 | -0.331920 | 0.066289 | 0.142851 | -0.183613 | -0.259661 | 0 |
| 14 | -1.750131 | -1.232270 | -0.386277 | -0.002139 | -0.909319 | 0.516002 | -0.559029 | -0.353133 | -0.143436 | -0.170585 | -0.055022 | 0 |
| 15 | 0.009909 | -3.464194 | 4.222059 | 0.778410 | 0.446583 | 1.659190 | 0.429358 | -0.390377 | 0.499373 | -0.041849 | 0.354199 | 0 |
| 16 | -1.146214 | -2.111590 | -1.185993 | -0.537097 | 0.517247 | 0.107555 | -0.267502 | -0.072676 | 0.088735 | -0.114849 | -0.359251 | 0 |
| 17 | -1.723924 | -1.293844 | -0.085023 | 0.197953 | -1.172703 | 0.210061 | -0.463852 | -0.542510 | -0.148757 | -0.148434 | 0.014237 | 0 |
| 18 | -0.322516 | -2.100750 | 2.014988 | 0.856887 | -0.384499 | -0.585060 | -0.638002 | 0.251468 | 0.085435 | 0.221362 | 0.063409 | 0 |
| 19 | -3.977446 | -1.171330 | 0.134423 | -0.394938 | 1.238367 | -1.018212 | 2.236757 | 2.061839 | -0.147960 | -0.296691 | -0.203067 | 0 |
| 20 | -2.066231 | -1.923692 | 1.726760 | 0.192129 | -0.693138 | 1.700350 | 1.187756 | -0.517069 | 0.473613 | -0.071320 | 0.087511 | 0 |
| 21 | -0.374719 | -2.071261 | 2.100620 | 1.369580 | -1.106210 | -1.909042 | -0.825242 | 0.112817 | 0.049800 | 0.190495 | 0.077678 | 0 |
| 22 | -1.182717 | -1.722051 | -0.634126 | 0.129705 | -0.467755 | -0.921688 | -0.670680 | -0.003336 | -0.043533 | 0.052566 | 0.045835 | 0 |
| 23 | -1.373889 | -1.899490 | 1.229482 | 0.065273 | -0.603294 | 1.676373 | 0.571713 | -1.249533 | 0.035502 | -0.227589 | -0.041483 | 0 |
| 24 | -0.151607 | -2.441351 | 2.652865 | 1.101502 | -0.515248 | -0.771171 | -0.065139 | -0.358824 | 0.154412 | 0.154986 | -0.081862 | 0 |
| 25 | -1.008282 | -1.785772 | -0.323846 | 0.015558 | -0.156833 | -0.294206 | -0.286456 | -0.204561 | 0.013758 | 0.028432 | 0.008690 | 0 |
| 26 | -1.539755 | -0.920739 | -0.076727 | 0.229274 | -1.209815 | 0.195572 | -0.186215 | -0.757890 | -0.221267 | -0.057763 | -0.082202 | 0 |
| 27 | -0.149081 | -2.163703 | 2.356188 | 1.087927 | -0.614977 | -1.008840 | -0.063041 | -0.395872 | 0.085767 | 0.189655 | -0.193659 | 0 |
| 28 | -3.522453 | -0.295340 | -0.131563 | 0.106544 | 0.795555 | -2.155864 | 0.570972 | 0.872353 | 0.159828 | 0.074185 | 0.073905 | 0 |
| 29 | -3.750821 | 0.334077 | 0.305371 | -0.066868 | -0.123965 | -0.081913 | 1.593498 | 0.836644 | 0.587575 | 0.324458 | -0.161960 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 4.478286 | 0.058006 | -0.649471 | -0.088195 | 0.118626 | 0.659358 | 0.266967 | 0.567403 | 0.102336 | -0.024233 | -0.381972 | 1 |
| 79 | 0.170862 | 1.358790 | -1.205971 | -0.252739 | -0.281869 | -0.894103 | -0.282680 | -0.682349 | 1.196525 | 0.057017 | -0.088277 | 0 |
| 80 | -0.741774 | 3.792263 | 1.001756 | -0.114708 | 0.169414 | -0.190819 | -0.738841 | 0.380811 | 0.242901 | -0.728481 | -0.133418 | 0 |
| 81 | 4.783592 | -0.039442 | -0.571420 | -0.437174 | -0.305688 | 0.507650 | 0.255915 | 0.662332 | 0.399101 | 0.165755 | -0.208843 | 1 |
| 82 | 0.468604 | 0.857043 | -0.465914 | -0.567331 | 0.154463 | 0.089279 | 0.716434 | -0.460483 | 0.249326 | -0.510813 | 0.146788 | 0 |
| 83 | 0.049049 | 3.578193 | 0.467543 | -0.046734 | -0.385710 | 0.173173 | -0.854540 | 0.448518 | 0.733687 | -0.389061 | -0.188824 | 1 |
| 84 | 4.321318 | 0.223923 | -0.486102 | -0.364267 | 0.106546 | 0.195278 | 0.118753 | 0.583612 | 0.130796 | 0.160910 | 0.002010 | 1 |
| 85 | 0.431876 | 0.652951 | -0.397503 | -0.165698 | 0.111361 | -0.074007 | 0.766345 | -0.547999 | 0.219227 | -0.420196 | 0.224023 | 0 |
| 86 | 0.163871 | 3.444238 | 0.875077 | -0.686279 | -0.739195 | -0.128048 | -0.988705 | 0.331269 | 0.832277 | -0.219633 | 0.006447 | 1 |
| 87 | 4.308394 | -0.510793 | -0.071237 | 0.081610 | -0.064405 | 0.288794 | 0.437382 | 0.287628 | 0.239987 | 0.221250 | -0.214290 | 1 |
| 88 | 0.374888 | 0.434577 | -0.275455 | -0.312340 | -0.006826 | -0.286532 | 0.806405 | -0.675913 | 0.187347 | -0.337647 | 0.131908 | 0 |
| 89 | 0.965129 | 1.776229 | 0.155755 | 0.493735 | -1.152353 | -0.162479 | -0.635827 | -0.218792 | -0.251284 | -0.424330 | -0.484578 | 1 |
| 90 | 4.501801 | -0.647759 | 0.554353 | 0.063195 | -0.532458 | -0.202094 | 0.047849 | 0.152657 | -0.481531 | -0.115392 | 0.274090 | 1 |
| 91 | 0.302127 | 0.695477 | -0.556860 | -0.057335 | -0.263189 | -0.761664 | 0.520295 | -0.492758 | 0.098087 | -0.243340 | 0.256112 | 0 |
| 92 | 0.560663 | 2.190915 | -0.134079 | 1.982935 | -0.302484 | 0.614406 | -0.394815 | 0.037583 | 0.054757 | -0.194704 | -0.358342 | 1 |
| 93 | 5.209716 | -0.955935 | -0.004407 | 0.882745 | -1.353030 | 0.143220 | 0.207319 | 0.662414 | -0.267244 | 0.056912 | 0.339676 | 1 |
| 94 | 0.852504 | 0.502216 | -0.786472 | -0.141094 | -0.673724 | -0.368209 | 0.341459 | -0.647959 | 0.920779 | 0.281212 | 0.226478 | 0 |
| 95 | -0.536637 | 3.183446 | 0.555293 | -0.132371 | 0.446959 | 0.328886 | -0.437220 | 0.124292 | 0.829259 | 0.386729 | -0.150190 | 1 |
| 96 | 5.392972 | -0.982346 | -0.251387 | 0.626731 | -1.402727 | -0.032393 | 0.215352 | 0.685838 | -0.289379 | -0.021494 | 0.230838 | 1 |
| 97 | 0.343826 | 0.806332 | -0.823722 | -0.045419 | -0.245563 | -1.016474 | 0.480068 | -0.434475 | 0.057321 | -0.161355 | 0.277174 | 0 |
| 98 | -0.505971 | 2.982403 | 1.135349 | -0.858154 | 0.370471 | 0.354728 | -0.056654 | 0.238542 | 0.793398 | 0.370396 | -0.050095 | 1 |
| 99 | 5.200854 | -0.465391 | -0.934898 | -0.229329 | -0.706470 | 0.441633 | 0.283417 | 0.788753 | 0.176302 | 0.312095 | 0.010279 | 1 |
| 100 | 0.394541 | 0.729059 | -0.743733 | -0.050470 | -0.240843 | -1.104907 | 0.540461 | -0.484981 | 0.077747 | -0.157398 | 0.298249 | 0 |
| 101 | 0.458918 | 2.561368 | 0.585001 | -0.066151 | -0.391602 | 0.021122 | -0.395216 | 0.270839 | -0.056034 | -0.088304 | -0.095397 | 1 |
| 102 | 5.649544 | -0.642510 | -1.127469 | -0.646168 | -1.015027 | 0.349322 | 0.214588 | 0.835208 | -0.265599 | 0.081387 | 0.008645 | 1 |
| 103 | 1.897351 | -0.025992 | -1.321641 | 0.414625 | -1.506061 | -0.673880 | 0.316538 | -0.508213 | 0.347161 | 0.023346 | 0.209247 | 0 |
| 104 | 1.424259 | 1.969073 | 0.670027 | -0.495009 | -0.719318 | 0.616269 | -0.328903 | 0.061407 | -0.508717 | -0.365781 | -0.133354 | 1 |
| 105 | 2.368058 | 0.864401 | 0.136113 | -0.420829 | 1.738602 | -0.927647 | 0.705547 | 0.658094 | 0.387713 | 0.168327 | 0.307485 | 1 |
| 106 | 0.490466 | 0.626758 | -0.786214 | 0.151879 | -0.268408 | -1.550888 | 0.600004 | -0.552887 | 0.117050 | -0.188364 | 0.309035 | 0 |
| 107 | 0.966710 | 1.803880 | 1.531750 | -0.437454 | -0.413422 | 0.607254 | 0.252542 | 0.029014 | 0.093774 | -0.310340 | -0.104726 | 1 |
108 rows × 12 columns
fig, ax = plt.subplots(figsize=(8,6))
ax.plot(range(1, migration_cont.shape[1]+1), migration_pca.explained_variance_ratio_, lw=2)
ax.scatter(range(1, migration_cont.shape[1]+1), migration_pca.explained_variance_ratio_, s=100)
ax.set_title('migration data: explained variance of components')
ax.set_xlabel('principal component')
ax.set_ylabel('explained variance')
plt.show()
for col, comp in zip(migration_cont.columns, migration_pca.components_[0]):
print col, comp
GDP_percapita_constant 0.444953784686 income_highest% -0.433783947612 income_lowest% 0.334104401133 poverty_headcount_1.90 -0.42408822848 death_rate -0.106912170586 unemployment 0.0948163627333 trade 0.0137408511283 pop_growth -0.419167628048 remittances 0.351743673355 net_bilateral_aid -0.0309566471764 FDI 0.0371274546902
for col, comp in zip(migration_cont.columns, migration_pca.components_[1]):
print col, comp
GDP_percapita_constant -0.012166667719 income_highest% 0.208096986157 income_lowest% -0.233634353696 poverty_headcount_1.90 0.0765655576751 death_rate -0.538365104029 unemployment -0.305100519113 trade 0.55055897166 pop_growth -0.0563939732101 remittances 0.396597653328 net_bilateral_aid -0.222691861952 FDI -0.0004280254334
for col, comp in zip(migration_cont.columns, migration_pca.components_[3]):
print col, comp
GDP_percapita_constant -0.037753685352 income_highest% 0.0175339147193 income_lowest% 0.028566403528 poverty_headcount_1.90 -0.0271247993477 death_rate -0.0639828470952 unemployment -0.0373895301668 trade 0.0462671590709 pop_growth -0.0730312142768 remittances 0.0420089510669 net_bilateral_aid 0.394499973419 FDI -0.909040403928
hue='threshold'¶sns.pairplot(data=migration_pcs, vars=['PC1','PC2','PC3'], hue='threshold', size=3)
plt.show()
def horn_parallel_analysis(shape, iters=1000, percentile=95):
pca = PCA(n_components=shape[1])
eigenvals = []
for i in range(iters):
rdata = np.random.normal(0,1,size=shape)
pca.fit(rdata)
eigenvals.append(pca.explained_variance_)
eigenvals = np.array(eigenvals)
return np.percentile(eigenvals, percentile, axis=0)
migration_pa = horn_parallel_analysis(migration_cont.shape, percentile=95)
migration_pa
array([ 1.78259703, 1.5597615 , 1.39057351, 1.27295842, 1.15745361,
1.05150508, 0.95585737, 0.86531285, 0.78163868, 0.68858056,
0.6034336 ])
.variance_explained_) against the parallel analysis random eigenvalue cutoffs¶fig, ax = plt.subplots(figsize=(8,6))
ax.plot(range(1, migration_cont.shape[1]+1), migration_pca.explained_variance_, lw=2)
ax.scatter(range(1, migration_cont.shape[1]+1), migration_pca.explained_variance_, s=50)
ax.plot(range(1, len(migration_pa)+1), migration_pa, lw=2, color='darkred')
ax.scatter(range(1, len(migration_pa)+1), migration_pa, s=40, color='darkred')
ax.set_title('Horns parallel analysis on migration data components')
ax.set_xlabel('principal component')
ax.set_ylabel('eigenvalue')
plt.show()
## Explore the noise on the original data
## should you Standarized the data?
## http://stats.stackexchange.com/questions/48360/is-standardization-needed-before-fitting-logistic-regression
sns.pairplot(data=migration_flows, hue='threshold')
plt.show()
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import cross_val_score, StratifiedKFold
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import train_test_split
## Define your x and y
columns_ = migration_flows.columns.tolist()
exclude_cols = ['threshold', 'Country', 'Year']
y = migration_flows.threshold.values
X = migration_flows[[i for i in columns_ if i not in exclude_cols]]
X = X.values
knn = KNeighborsClassifier()
params = {
'n_neighbors':range(1,20),
'weights':['uniform','distance']
}
knn_gs = GridSearchCV(knn, params, cv=5, verbose=1)
knn_gs.fit(X, y)
print knn_gs.best_params_
best_knn = knn_gs.best_estimator_
Fitting 5 folds for each of 38 candidates, totalling 190 fits
{'n_neighbors': 10, 'weights': 'uniform'}
[Parallel(n_jobs=1)]: Done 190 out of 190 | elapsed: 0.8s finished
cv_indices = StratifiedKFold(y, n_folds=5)
## StratifiedKFold = Provides train/test indices to split data in train/test sets.
## http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html
logreg = LogisticRegression()
lr_scores_test = []
lr_scores_train = []
knn_scores_test = []
knn_scores_train = []
for train_inds, test_inds in cv_indices:
Xtr, ytr = X[train_inds, :], y[train_inds]
Xte, yte = X[test_inds, :], y[test_inds]
print 'Xtrain and ytrain shapes:\n', Xtr.shape, ytr.shape
print 'Xtest and ytest shapes:\n', Xte.shape, yte.shape
best_knn.fit(Xtr, ytr)
knn_scores_test.append(best_knn.score(Xte, yte))
knn_scores_train.append(best_knn.score(Xtr, ytr))
'''best_knn.score = Returns the mean accuracy on the given test data and labels'''
y_knn_predict = best_knn.predict(Xte)
logreg.fit(Xtr, ytr)
lr_scores_test.append(logreg.score(Xte, yte))
lr_scores_train.append(logreg.score(Xtr, ytr))
'''logreg.score = Returns the mean accuracy on the given test data and labels'''
y_log_predict = logreg.predict(Xte)
print "\n======\n"
print 'KNN accuracy scores on test:\n', knn_scores_test
print 'KNN mean of accuracy scores on test:\n', np.mean(knn_scores_test)
print 'KNN mean of accuracy scores on train :\n', np.mean(knn_scores_train)
print "\n======\n"
print 'Logistic Regression accuracy scores on test:\n', lr_scores_test
print 'Logistic Regression mean of accuracy scores on test:\n', np.mean(lr_scores_test)
print 'Logistic Regression mean of accuracy scores on train:\n', np.mean(lr_scores_train)
print "\n======\n"
print 'Baseline accuracy:\n ', np.mean(y)
Xtrain and ytrain shapes: (85, 12) (85,) Xtest and ytest shapes: (23, 12) (23,) Xtrain and ytrain shapes: (86, 12) (86,) Xtest and ytest shapes: (22, 12) (22,) Xtrain and ytrain shapes: (87, 12) (87,) Xtest and ytest shapes: (21, 12) (21,) Xtrain and ytrain shapes: (87, 12) (87,) Xtest and ytest shapes: (21, 12) (21,) Xtrain and ytrain shapes: (87, 12) (87,) Xtest and ytest shapes: (21, 12) (21,) ====== KNN accuracy scores on test: [0.60869565217391308, 0.68181818181818177, 0.61904761904761907, 0.52380952380952384, 0.61904761904761907] KNN mean of accuracy scores on test: 0.610483719179 KNN mean of accuracy scores on train : 0.662051354624 ====== Logistic Regression accuracy scores on test: [0.65217391304347827, 0.68181818181818177, 0.66666666666666663, 0.61904761904761907, 0.66666666666666663] Logistic Regression mean of accuracy scores on test: 0.657274609449 Logistic Regression mean of accuracy scores on train: 0.657399562872 ====== Baseline accuracy: 0.333333333333
## Define your x and y
## For your X = only use the number of PCA's that have the greatest explanatory power
columns_ = migration_pcs.columns.tolist()
exclude_cols = ['Year', 'Country', 'PC5','PC6','PC7','PC8','PC9','PC10','PC11', 'threshold']
ypc = migration_pcs.threshold.values
Xpc = migration_pcs[[i for i in columns_ if i not in exclude_cols]]
Xpc = Xpc.values
knn = KNeighborsClassifier()
params = {
'n_neighbors':range(1,20),
'weights':['uniform','distance']
}
knn_gs_pc = GridSearchCV(knn, params, cv=5, verbose=1)
knn_gs_pc.fit(Xpc, ypc)
print knn_gs_pc.best_params_
best_knn_pc = knn_gs_pc.best_estimator_
Fitting 5 folds for each of 38 candidates, totalling 190 fits
{'n_neighbors': 17, 'weights': 'uniform'}
[Parallel(n_jobs=1)]: Done 190 out of 190 | elapsed: 0.9s finished
cv_indices_pc = StratifiedKFold(ypc, n_folds=5)
logreg_pc = LogisticRegression()
lr_scores_test_pc = []
lr_scores_train_pc = []
knn_scores_test_pc = []
knn_scores_train_pc = []
for train_inds, test_inds in cv_indices_pc:
Xtr_pc, ytr_pc = Xpc[train_inds, :], ypc[train_inds]
Xte_pc, yte_pc = Xpc[test_inds, :], ypc[test_inds]
print 'Xtrain and ytrain shapes:\n', Xtr_pc.shape, ytr_pc.shape
print 'Xtest and ytest shapes:\n', Xte_pc.shape, yte_pc.shape
best_knn_pc.fit(Xtr_pc, ytr_pc)
knn_scores_test_pc.append(best_knn_pc.score(Xte_pc, yte_pc))
knn_scores_train_pc.append(best_knn_pc.score(Xtr_pc, ytr_pc))
'''best_knn.score = Returns the mean accuracy on the given test data and labels'''
y_knn_predict_pc = best_knn_pc.predict(Xte_pc)
logreg_pc.fit(Xtr_pc, ytr_pc)
lr_scores_test_pc.append(logreg_pc.score(Xte_pc, yte_pc))
lr_scores_train_pc.append(logreg_pc.score(Xtr_pc, ytr_pc))
'''logreg.score = Returns the mean accuracy on the given test data and labels'''
y_log_predict_pc = logreg_pc.predict(Xte_pc)
print "\n======\n"
print 'KNN accuracy scores on test:\n', knn_scores_test_pc
print 'KNN mean of accuracy scores on test:\n', np.mean(knn_scores_test_pc)
print 'KNN mean of accuracy scores on train :\n', np.mean(knn_scores_train_pc)
print "\n======\n"
print 'Logistic Regression accuracy scores on test:\n', lr_scores_test_pc
print 'Logistic Regression mean of accuracy scores on test:\n', np.mean(lr_scores_test_pc)
print 'Logistic Regression mean of accuracy scores on train:\n', np.mean(lr_scores_train_pc)
print "\n======\n"
print 'Baseline accuracy:\n ', np.mean(ypc)
Xtrain and ytrain shapes: (85, 4) (85,) Xtest and ytest shapes: (23, 4) (23,) Xtrain and ytrain shapes: (86, 4) (86,) Xtest and ytest shapes: (22, 4) (22,) Xtrain and ytrain shapes: (87, 4) (87,) Xtest and ytest shapes: (21, 4) (21,) Xtrain and ytrain shapes: (87, 4) (87,) Xtest and ytest shapes: (21, 4) (21,) Xtrain and ytrain shapes: (87, 4) (87,) Xtest and ytest shapes: (21, 4) (21,) ====== KNN accuracy scores on test: [0.69565217391304346, 0.81818181818181823, 0.95238095238095233, 0.90476190476190477, 0.90476190476190477] KNN mean of accuracy scores on test: 0.8551477508 KNN mean of accuracy scores on train : 0.872755633127 ====== Logistic Regression accuracy scores on test: [0.78260869565217395, 0.86363636363636365, 0.95238095238095233, 0.8571428571428571, 0.42857142857142855] Logistic Regression mean of accuracy scores on test: 0.776868059477 Logistic Regression mean of accuracy scores on train: 0.937553658191 ====== Baseline accuracy: 0.333333333333
'''the mean of the accuracy score on the test data has a significant increase from '''
print 'KNN mean of accuracy scores on test:\n', np.mean(knn_scores_test)
print 'KNN mean of accuracy scores on test PC:\n', np.mean(knn_scores_test_pc)
print "Increase of accuracy of:", (np.mean(knn_scores_test_pc) - np.mean(knn_scores_test))
KNN mean of accuracy scores on test: 0.610483719179 KNN mean of accuracy scores on test PC: 0.8551477508 Increase of accuracy of: 0.244664031621
# Load Confusion Matrix
from sklearn.metrics import confusion_matrix
def confus_mat(ytrue, ypred_method, what_predict):
what_predict = str(what_predict)
confmat = confusion_matrix(y_true=ytrue, y_pred=ypred_method)
confusion = pd.DataFrame(confmat, index=['is_not_' + what_predict, 'is_' + what_predict],
columns=['predicted_is_not_'+ what_predict, 'predicted_is_'+what_predict])
return confusion
# Load Classification Report
from sklearn.metrics import classification_report
def class_report(ytrue, ypred):
cls_rep = classification_report(yte, y_knn_predict)
print cls_rep
## Confuion Matrix for knn
confus_mat(yte, y_knn_predict, 'threshold')
| predicted_is_not_threshold | predicted_is_threshold | |
|---|---|---|
| is_not_threshold | 13 | 1 |
| is_threshold | 7 | 0 |
## Classification report for knn
class_report(yte, y_knn_predict)
precision recall f1-score support
0 0.65 0.93 0.76 14
1 0.00 0.00 0.00 7
avg / total 0.43 0.62 0.51 21
## Confusion Matrix for logistic
confus_mat(yte, y_log_predict, 'threshold')
| predicted_is_not_threshold | predicted_is_threshold | |
|---|---|---|
| is_not_threshold | 14 | 0 |
| is_threshold | 7 | 0 |
## Classification report for logistic
class_report(yte, y_log_predict)
precision recall f1-score support
0 0.65 0.93 0.76 14
1 0.00 0.00 0.00 7
avg / total 0.43 0.62 0.51 21
## Confuion Matrix for knn with PC
confus_mat(yte, y_knn_predict_pc, 'threshold')
| predicted_is_not_threshold | predicted_is_threshold | |
|---|---|---|
| is_not_threshold | 12 | 2 |
| is_threshold | 0 | 7 |
## Classification report for knn with PC
class_report(yte, y_knn_predict_pc)
precision recall f1-score support
0 0.65 0.93 0.76 14
1 0.00 0.00 0.00 7
avg / total 0.43 0.62 0.51 21
## Confuion Matrix for log with PC
confus_mat(yte, y_log_predict_pc, 'threshold')
| predicted_is_not_threshold | predicted_is_threshold | |
|---|---|---|
| is_not_threshold | 2 | 12 |
| is_threshold | 0 | 7 |
## Classification report for knn with PC
class_report(yte, y_log_predict_pc)
precision recall f1-score support
0 0.65 0.93 0.76 14
1 0.00 0.00 0.00 7
avg / total 0.43 0.62 0.51 21